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: CDANN
	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
	beta1: 0.5
	class_balanced: False
	d_steps_per_g_step: 1
	data_augmentation: True
	grad_penalty: 0.0
	lambda: 1.0
	lr: 5e-05
	lr_d: 5e-05
	lr_g: 5e-05
	mlp_depth: 3
	mlp_dropout: 0.0
	mlp_width: 256
	nonlinear_classifier: False
	resnet18: True
	resnet_dropout: 0.0
	weight_decay: 0.0
	weight_decay_d: 0.0
	weight_decay_g: 0.0
step_vals {'gen_loss': 0.08984607458114624}
Iteration: 0
out-of-domain val
accuracy 0.585 	 roc_auc_score 0.680
confusion_matrix
[[ 75 213]
 [ 26 262]]
classification_report
              precision    recall  f1-score   support

           0       0.74      0.26      0.39       288
           1       0.55      0.91      0.69       288

    accuracy                           0.59       576
   macro avg       0.65      0.59      0.54       576
weighted avg       0.65      0.59      0.54       576

VAL * Acc@1 58.507
 * Acc@1 58.507 Acc@5 0.000
accuracy 0.917 	 size: 144 	 dog(outdoor)
accuracy 0.903 	 size: 144 	 dog(indoor)
accuracy 0.326 	 size: 144 	 cat(indoor)
accuracy 0.194 	 size: 144 	 cat(outdoor)
step_vals {'disc_loss': 0.6802048683166504}
step_vals {'gen_loss': 0.040451645851135254}
step_vals {'disc_loss': 0.6903998255729675}
step_vals {'gen_loss': -0.07392126321792603}
step_vals {'disc_loss': 0.6994688510894775}
step_vals {'gen_loss': -0.1568622589111328}
step_vals {'disc_loss': 0.6901286840438843}
step_vals {'gen_loss': -0.17745453119277954}
step_vals {'disc_loss': 0.6761126518249512}
step_vals {'gen_loss': -0.09885132312774658}
step_vals {'disc_loss': 0.6804075837135315}
step_vals {'gen_loss': -0.18339329957962036}
step_vals {'disc_loss': 0.6748870611190796}
step_vals {'gen_loss': -0.2049465775489807}
step_vals {'disc_loss': 0.687477707862854}
step_vals {'gen_loss': -0.06899476051330566}
step_vals {'disc_loss': 0.6898812055587769}
step_vals {'gen_loss': -0.2785142660140991}
step_vals {'disc_loss': 0.6833387613296509}
step_vals {'gen_loss': -0.30315932631492615}
Iteration: 20
out-of-domain val
accuracy 0.774 	 roc_auc_score 0.876
confusion_matrix
[[188 100]
 [ 30 258]]
classification_report
              precision    recall  f1-score   support

           0       0.86      0.65      0.74       288
           1       0.72      0.90      0.80       288

    accuracy                           0.77       576
   macro avg       0.79      0.77      0.77       576
weighted avg       0.79      0.77      0.77       576

VAL * Acc@1 77.431
 * Acc@1 77.431 Acc@5 0.000
accuracy 0.986 	 size: 144 	 dog(outdoor)
accuracy 0.806 	 size: 144 	 dog(indoor)
accuracy 0.757 	 size: 144 	 cat(indoor)
accuracy 0.549 	 size: 144 	 cat(outdoor)
step_vals {'disc_loss': 0.6968709826469421}
step_vals {'gen_loss': -0.3136560320854187}
step_vals {'disc_loss': 0.6937681436538696}
step_vals {'gen_loss': -0.26745009422302246}
step_vals {'disc_loss': 0.690022349357605}
step_vals {'gen_loss': -0.08581751585006714}
step_vals {'disc_loss': 0.6785135269165039}
step_vals {'gen_loss': -0.00929027795791626}
step_vals {'disc_loss': 0.6991098523139954}
step_vals {'gen_loss': -0.1922040581703186}
step_vals {'disc_loss': 0.7041083574295044}
step_vals {'gen_loss': -0.21992748975753784}
step_vals {'disc_loss': 0.6901191473007202}
step_vals {'gen_loss': -0.3218638598918915}
step_vals {'disc_loss': 0.6944641470909119}
step_vals {'gen_loss': -0.32447001338005066}
step_vals {'disc_loss': 0.6961036920547485}
step_vals {'gen_loss': -0.2524273693561554}
step_vals {'disc_loss': 0.6797200441360474}
step_vals {'gen_loss': -0.21515440940856934}
Iteration: 40
out-of-domain val
accuracy 0.837 	 roc_auc_score 0.912
confusion_matrix
[[242  46]
 [ 48 240]]
classification_report
              precision    recall  f1-score   support

           0       0.83      0.84      0.84       288
           1       0.84      0.83      0.84       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 83.681
 * Acc@1 83.681 Acc@5 0.000
accuracy 0.917 	 size: 144 	 dog(outdoor)
accuracy 0.882 	 size: 144 	 cat(indoor)
accuracy 0.799 	 size: 144 	 cat(outdoor)
accuracy 0.750 	 size: 144 	 dog(indoor)
step_vals {'disc_loss': 0.6861652135848999}
step_vals {'gen_loss': -0.22138553857803345}
step_vals {'disc_loss': 0.682860255241394}
step_vals {'gen_loss': -0.20359954237937927}
step_vals {'disc_loss': 0.6987673044204712}
step_vals {'gen_loss': -0.31709402799606323}
step_vals {'disc_loss': 0.6758648157119751}
step_vals {'gen_loss': -0.3210448622703552}
step_vals {'disc_loss': 0.7158435583114624}
step_vals {'gen_loss': -0.30229562520980835}
step_vals {'disc_loss': 0.6916306018829346}
step_vals {'gen_loss': -0.3407154083251953}
step_vals {'disc_loss': 0.6961034536361694}
step_vals {'gen_loss': -0.4010781943798065}
step_vals {'disc_loss': 0.6927676200866699}
step_vals {'gen_loss': -0.44529402256011963}
step_vals {'disc_loss': 0.7114756107330322}
step_vals {'gen_loss': -0.0825682282447815}
step_vals {'disc_loss': 0.7002460956573486}
step_vals {'gen_loss': -0.20652762055397034}
Iteration: 60
out-of-domain val
accuracy 0.852 	 roc_auc_score 0.926
confusion_matrix
[[238  50]
 [ 35 253]]
classification_report
              precision    recall  f1-score   support

           0       0.87      0.83      0.85       288
           1       0.83      0.88      0.86       288

    accuracy                           0.85       576
   macro avg       0.85      0.85      0.85       576
weighted avg       0.85      0.85      0.85       576

VAL * Acc@1 85.243
 * Acc@1 85.243 Acc@5 0.000
accuracy 0.958 	 size: 144 	 dog(outdoor)
accuracy 0.889 	 size: 144 	 cat(indoor)
accuracy 0.799 	 size: 144 	 dog(indoor)
accuracy 0.764 	 size: 144 	 cat(outdoor)
step_vals {'disc_loss': 0.6998758316040039}
step_vals {'gen_loss': -0.46734654903411865}
step_vals {'disc_loss': 0.6998577117919922}
step_vals {'gen_loss': -0.15708881616592407}
step_vals {'disc_loss': 0.6953407526016235}
step_vals {'gen_loss': -0.24141085147857666}
step_vals {'disc_loss': 0.7118141651153564}
step_vals {'gen_loss': -0.4310609698295593}
step_vals {'disc_loss': 0.7040573358535767}
step_vals {'gen_loss': -0.27014291286468506}
step_vals {'disc_loss': 0.6886176466941833}
step_vals {'gen_loss': -0.32281073927879333}
step_vals {'disc_loss': 0.7161538600921631}
step_vals {'gen_loss': -0.41716647148132324}
step_vals {'disc_loss': 0.7076867818832397}
step_vals {'gen_loss': -0.35372936725616455}
step_vals {'disc_loss': 0.6831676363945007}
step_vals {'gen_loss': -0.4293037950992584}
step_vals {'disc_loss': 0.7050008773803711}
step_vals {'gen_loss': -0.44895726442337036}
Iteration: 80
out-of-domain val
accuracy 0.788 	 roc_auc_score 0.914
confusion_matrix
[[265  23]
 [ 99 189]]
classification_report
              precision    recall  f1-score   support

           0       0.73      0.92      0.81       288
           1       0.89      0.66      0.76       288

    accuracy                           0.79       576
   macro avg       0.81      0.79      0.78       576
weighted avg       0.81      0.79      0.78       576

VAL * Acc@1 78.819
 * Acc@1 78.819 Acc@5 0.000
accuracy 0.993 	 size: 144 	 cat(indoor)
accuracy 0.847 	 size: 144 	 cat(outdoor)
accuracy 0.778 	 size: 144 	 dog(outdoor)
accuracy 0.535 	 size: 144 	 dog(indoor)
