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)', 800), ('dog(outdoor)', 800), ('cat(outdoor)', 50), ('dog(indoor)', 50)]
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': 0.015797436237335205}
Iteration: 0
out-of-domain val
accuracy 0.568 	 roc_auc_score 0.611
confusion_matrix
[[134 154]
 [ 95 193]]
classification_report
              precision    recall  f1-score   support

           0       0.59      0.47      0.52       288
           1       0.56      0.67      0.61       288

    accuracy                           0.57       576
   macro avg       0.57      0.57      0.56       576
weighted avg       0.57      0.57      0.56       576

VAL * Acc@1 56.771
 * Acc@1 56.771 Acc@5 0.000
accuracy 0.681 	 size: 144 	 dog(indoor)
accuracy 0.660 	 size: 144 	 dog(outdoor)
accuracy 0.528 	 size: 144 	 cat(indoor)
accuracy 0.403 	 size: 144 	 cat(outdoor)
step_vals {'disc_loss': 0.6785521507263184}
step_vals {'gen_loss': 0.03433471918106079}
step_vals {'disc_loss': 0.6885552406311035}
step_vals {'gen_loss': 0.005720198154449463}
step_vals {'disc_loss': 0.6891220808029175}
step_vals {'gen_loss': -0.1278160810470581}
step_vals {'disc_loss': 0.7065330147743225}
step_vals {'gen_loss': -0.18534690141677856}
step_vals {'disc_loss': 0.6947341561317444}
step_vals {'gen_loss': -0.17704463005065918}
step_vals {'disc_loss': 0.6785733699798584}
step_vals {'gen_loss': -0.19323629140853882}
step_vals {'disc_loss': 0.7101985216140747}
step_vals {'gen_loss': -0.1932656168937683}
step_vals {'disc_loss': 0.6961143016815186}
step_vals {'gen_loss': 0.130409836769104}
step_vals {'disc_loss': 0.7085248231887817}
step_vals {'gen_loss': -0.3004419803619385}
step_vals {'disc_loss': 0.6836311221122742}
step_vals {'gen_loss': -0.17015278339385986}
Iteration: 20
out-of-domain val
accuracy 0.752 	 roc_auc_score 0.874
confusion_matrix
[[257  31]
 [112 176]]
classification_report
              precision    recall  f1-score   support

           0       0.70      0.89      0.78       288
           1       0.85      0.61      0.71       288

    accuracy                           0.75       576
   macro avg       0.77      0.75      0.75       576
weighted avg       0.77      0.75      0.75       576

VAL * Acc@1 75.174
 * Acc@1 75.174 Acc@5 0.000
accuracy 0.951 	 size: 144 	 cat(indoor)
accuracy 0.833 	 size: 144 	 cat(outdoor)
accuracy 0.736 	 size: 144 	 dog(outdoor)
accuracy 0.486 	 size: 144 	 dog(indoor)
step_vals {'disc_loss': 0.7112206220626831}
step_vals {'gen_loss': -0.22177165746688843}
step_vals {'disc_loss': 0.7040882110595703}
step_vals {'gen_loss': -0.3017145097255707}
step_vals {'disc_loss': 0.7099059224128723}
step_vals {'gen_loss': -0.3399936854839325}
step_vals {'disc_loss': 0.6782280802726746}
step_vals {'gen_loss': -0.30588990449905396}
step_vals {'disc_loss': 0.6915774345397949}
step_vals {'gen_loss': -0.3605460524559021}
step_vals {'disc_loss': 0.7096638679504395}
step_vals {'gen_loss': -0.2865074872970581}
step_vals {'disc_loss': 0.6929071545600891}
step_vals {'gen_loss': -0.29275429248809814}
step_vals {'disc_loss': 0.6881821155548096}
step_vals {'gen_loss': -0.3602893650531769}
step_vals {'disc_loss': 0.6942754983901978}
step_vals {'gen_loss': -0.26357460021972656}
step_vals {'disc_loss': 0.698184609413147}
step_vals {'gen_loss': -0.26001355051994324}
Iteration: 40
out-of-domain val
accuracy 0.799 	 roc_auc_score 0.886
confusion_matrix
[[232  56]
 [ 60 228]]
classification_report
              precision    recall  f1-score   support

           0       0.79      0.81      0.80       288
           1       0.80      0.79      0.80       288

    accuracy                           0.80       576
   macro avg       0.80      0.80      0.80       576
weighted avg       0.80      0.80      0.80       576

VAL * Acc@1 79.861
 * Acc@1 79.861 Acc@5 0.000
accuracy 0.931 	 size: 144 	 dog(outdoor)
accuracy 0.910 	 size: 144 	 cat(indoor)
accuracy 0.701 	 size: 144 	 cat(outdoor)
accuracy 0.653 	 size: 144 	 dog(indoor)
step_vals {'disc_loss': 0.7137371301651001}
step_vals {'gen_loss': -0.3439679741859436}
step_vals {'disc_loss': 0.7004516124725342}
step_vals {'gen_loss': -0.3151342570781708}
step_vals {'disc_loss': 0.6871815919876099}
step_vals {'gen_loss': -0.26265138387680054}
step_vals {'disc_loss': 0.7050610780715942}
step_vals {'gen_loss': -0.34556031227111816}
step_vals {'disc_loss': 0.69384765625}
step_vals {'gen_loss': -0.2797626256942749}
step_vals {'disc_loss': 0.7257359623908997}
step_vals {'gen_loss': 0.023209810256958008}
step_vals {'disc_loss': 0.7036615014076233}
step_vals {'gen_loss': -0.23334646224975586}
step_vals {'disc_loss': 0.7249671816825867}
step_vals {'gen_loss': -0.28892916440963745}
step_vals {'disc_loss': 0.6993197798728943}
step_vals {'gen_loss': -0.4656965136528015}
step_vals {'disc_loss': 0.6889588832855225}
step_vals {'gen_loss': -0.4446096420288086}
Iteration: 60
out-of-domain val
accuracy 0.811 	 roc_auc_score 0.900
confusion_matrix
[[232  56]
 [ 53 235]]
classification_report
              precision    recall  f1-score   support

           0       0.81      0.81      0.81       288
           1       0.81      0.82      0.81       288

    accuracy                           0.81       576
   macro avg       0.81      0.81      0.81       576
weighted avg       0.81      0.81      0.81       576

VAL * Acc@1 81.076
 * Acc@1 81.076 Acc@5 0.000
accuracy 0.931 	 size: 144 	 dog(outdoor)
accuracy 0.903 	 size: 144 	 cat(indoor)
accuracy 0.708 	 size: 144 	 cat(outdoor)
accuracy 0.701 	 size: 144 	 dog(indoor)
step_vals {'disc_loss': 0.7065294981002808}
step_vals {'gen_loss': -0.2938307225704193}
step_vals {'disc_loss': 0.694064736366272}
step_vals {'gen_loss': -0.5362823009490967}
step_vals {'disc_loss': 0.7120896577835083}
step_vals {'gen_loss': -0.43074822425842285}
step_vals {'disc_loss': 0.7213841080665588}
step_vals {'gen_loss': -0.4662219285964966}
step_vals {'disc_loss': 0.7010679244995117}
step_vals {'gen_loss': -0.4545595049858093}
step_vals {'disc_loss': 0.7299172878265381}
step_vals {'gen_loss': -0.3713516592979431}
step_vals {'disc_loss': 0.7165907621383667}
step_vals {'gen_loss': -0.5015023350715637}
step_vals {'disc_loss': 0.7207683324813843}
step_vals {'gen_loss': -0.5599946975708008}
step_vals {'disc_loss': 0.753105878829956}
step_vals {'gen_loss': -0.45562416315078735}
step_vals {'disc_loss': 0.7731503248214722}
step_vals {'gen_loss': -0.25330162048339844}
Iteration: 80
out-of-domain val
accuracy 0.542 	 roc_auc_score 0.866
confusion_matrix
[[284   4]
 [260  28]]
classification_report
              precision    recall  f1-score   support

           0       0.52      0.99      0.68       288
           1       0.88      0.10      0.18       288

    accuracy                           0.54       576
   macro avg       0.70      0.54      0.43       576
weighted avg       0.70      0.54      0.43       576

VAL * Acc@1 54.167
 * Acc@1 54.167 Acc@5 0.000
accuracy 1.000 	 size: 144 	 cat(indoor)
accuracy 0.972 	 size: 144 	 cat(outdoor)
accuracy 0.104 	 size: 144 	 dog(outdoor)
accuracy 0.090 	 size: 144 	 dog(indoor)
