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)', 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
	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': 0.08119428157806396}
Iteration: 0
out-of-domain val
accuracy 0.583 	 roc_auc_score 0.662
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 0.382 	 size: 144 	 cat(indoor)
accuracy 0.194 	 size: 144 	 cat(outdoor)
step_vals {'disc_loss': 0.6962373852729797}
step_vals {'gen_loss': -0.07415300607681274}
step_vals {'disc_loss': 0.7015805840492249}
step_vals {'gen_loss': -0.2324325144290924}
step_vals {'disc_loss': 0.7005144357681274}
step_vals {'gen_loss': -0.2607247829437256}
step_vals {'disc_loss': 0.7048845291137695}
step_vals {'gen_loss': -0.337433397769928}
step_vals {'disc_loss': 0.7070872783660889}
step_vals {'gen_loss': -0.30275076627731323}
step_vals {'disc_loss': 0.7045233845710754}
step_vals {'gen_loss': -0.4567685127258301}
step_vals {'disc_loss': 0.6956547498703003}
step_vals {'gen_loss': -0.5610063672065735}
step_vals {'disc_loss': 0.6916602849960327}
step_vals {'gen_loss': -0.5134340524673462}
step_vals {'disc_loss': 0.7209299206733704}
step_vals {'gen_loss': -0.33917921781539917}
step_vals {'disc_loss': 0.7133477926254272}
step_vals {'gen_loss': -0.25701579451560974}
Iteration: 20
out-of-domain val
accuracy 0.750 	 roc_auc_score 0.861
confusion_matrix
[[248  40]
 [104 184]]
classification_report
              precision    recall  f1-score   support

           0       0.70      0.86      0.78       288
           1       0.82      0.64      0.72       288

    accuracy                           0.75       576
   macro avg       0.76      0.75      0.75       576
weighted avg       0.76      0.75      0.75       576

VAL * Acc@1 75.000
 * Acc@1 75.000 Acc@5 0.000
accuracy 0.965 	 size: 144 	 cat(indoor)
accuracy 0.826 	 size: 144 	 dog(outdoor)
accuracy 0.757 	 size: 144 	 cat(outdoor)
accuracy 0.451 	 size: 144 	 dog(indoor)
step_vals {'disc_loss': 0.7291566133499146}
step_vals {'gen_loss': -0.3978423476219177}
step_vals {'disc_loss': 0.7075356841087341}
step_vals {'gen_loss': -0.5577430725097656}
step_vals {'disc_loss': 0.7206394672393799}
step_vals {'gen_loss': -0.4224810004234314}
step_vals {'disc_loss': 0.7284882068634033}
step_vals {'gen_loss': -0.5902605056762695}
step_vals {'disc_loss': 0.7412824034690857}
step_vals {'gen_loss': -0.6231982707977295}
step_vals {'disc_loss': 0.7259193062782288}
step_vals {'gen_loss': -0.49633604288101196}
step_vals {'disc_loss': 0.7580404877662659}
step_vals {'gen_loss': -0.41062530875205994}
step_vals {'disc_loss': 0.744574785232544}
step_vals {'gen_loss': -0.2556997239589691}
step_vals {'disc_loss': 0.7532144784927368}
step_vals {'gen_loss': -0.6500642895698547}
step_vals {'disc_loss': 0.7542233467102051}
step_vals {'gen_loss': -0.4859628975391388}
Iteration: 40
out-of-domain val
accuracy 0.776 	 roc_auc_score 0.869
confusion_matrix
[[187 101]
 [ 28 260]]
classification_report
              precision    recall  f1-score   support

           0       0.87      0.65      0.74       288
           1       0.72      0.90      0.80       288

    accuracy                           0.78       576
   macro avg       0.79      0.78      0.77       576
weighted avg       0.79      0.78      0.77       576

VAL * Acc@1 77.604
 * Acc@1 77.604 Acc@5 0.000
accuracy 0.972 	 size: 144 	 dog(outdoor)
accuracy 0.833 	 size: 144 	 dog(indoor)
accuracy 0.826 	 size: 144 	 cat(indoor)
accuracy 0.472 	 size: 144 	 cat(outdoor)
step_vals {'disc_loss': 0.7407434582710266}
step_vals {'gen_loss': -0.559050440788269}
step_vals {'disc_loss': 0.7376236915588379}
step_vals {'gen_loss': -0.43290963768959045}
step_vals {'disc_loss': 0.7653830647468567}
step_vals {'gen_loss': -0.622907817363739}
step_vals {'disc_loss': 0.7578580379486084}
step_vals {'gen_loss': -0.4518306255340576}
step_vals {'disc_loss': 0.7344623804092407}
step_vals {'gen_loss': -0.6906138062477112}
step_vals {'disc_loss': 0.7926247119903564}
step_vals {'gen_loss': -0.6501583456993103}
step_vals {'disc_loss': 0.7640376091003418}
step_vals {'gen_loss': -0.6142184138298035}
step_vals {'disc_loss': 0.7953475713729858}
step_vals {'gen_loss': -0.6129192113876343}
step_vals {'disc_loss': 0.8176779747009277}
step_vals {'gen_loss': -0.6050839424133301}
step_vals {'disc_loss': 0.8188015222549438}
step_vals {'gen_loss': -0.6094150543212891}
Iteration: 60
out-of-domain val
accuracy 0.774 	 roc_auc_score 0.861
confusion_matrix
[[221  67]
 [ 63 225]]
classification_report
              precision    recall  f1-score   support

           0       0.78      0.77      0.77       288
           1       0.77      0.78      0.78       288

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

VAL * Acc@1 77.431
 * Acc@1 77.431 Acc@5 0.000
accuracy 0.938 	 size: 144 	 dog(outdoor)
accuracy 0.903 	 size: 144 	 cat(indoor)
accuracy 0.632 	 size: 144 	 cat(outdoor)
accuracy 0.625 	 size: 144 	 dog(indoor)
step_vals {'disc_loss': 0.8444551825523376}
step_vals {'gen_loss': -0.4773508906364441}
step_vals {'disc_loss': 0.8570025563240051}
step_vals {'gen_loss': -0.5983515381813049}
step_vals {'disc_loss': 0.8912339806556702}
step_vals {'gen_loss': -0.05867910385131836}
step_vals {'disc_loss': 0.8355634808540344}
step_vals {'gen_loss': -0.2203129529953003}
step_vals {'disc_loss': 0.7722600698471069}
step_vals {'gen_loss': -0.5825349688529968}
step_vals {'disc_loss': 0.7675096988677979}
step_vals {'gen_loss': -0.6025710105895996}
step_vals {'disc_loss': 0.789069414138794}
step_vals {'gen_loss': -0.5901241302490234}
step_vals {'disc_loss': 0.7809649705886841}
step_vals {'gen_loss': -0.7133051753044128}
step_vals {'disc_loss': 0.8178759217262268}
step_vals {'gen_loss': -0.6322111487388611}
step_vals {'disc_loss': 0.7976240515708923}
step_vals {'gen_loss': -0.5734124779701233}
Iteration: 80
out-of-domain val
accuracy 0.714 	 roc_auc_score 0.871
confusion_matrix
[[131 157]
 [  8 280]]
classification_report
              precision    recall  f1-score   support

           0       0.94      0.45      0.61       288
           1       0.64      0.97      0.77       288

    accuracy                           0.71       576
   macro avg       0.79      0.71      0.69       576
weighted avg       0.79      0.71      0.69       576

VAL * Acc@1 71.354
 * Acc@1 71.354 Acc@5 0.000
accuracy 0.993 	 size: 144 	 dog(outdoor)
accuracy 0.951 	 size: 144 	 dog(indoor)
accuracy 0.646 	 size: 144 	 cat(indoor)
accuracy 0.264 	 size: 144 	 cat(outdoor)
