Environment:
	Python: 3.7.4
	PyTorch: 1.8.1+cu102
	Torchvision: 0.9.1+cu102
	CUDA: 10.2
	CUDNN: 7605
	NumPy: 1.20.3
	PIL: 8.2.0
Args:
	adapt_algorithm: T3A
	algorithm: ERM
	checkpoint_freq: None
	data_dir: ./dataset
	dataset: PACS
	holdout_fraction: 0.2
	hparams: {"backbone": "resnet50"}
	hparams_seed: 0
	input_dir: test_log
	output_dir: test_log
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: False
	steps: None
	task: domain_generalization
	test_envs: [0]
	trial_seed: 0
	uda_holdout_fraction: 0
HParams:
	backbone: resnet50
	batch_size: 32
	class_balanced: False
	data_augmentation: True
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0.0
	weight_decay: 0.0
Base model's results
env0_in_acc   env0_in_ent   env0_out_acc  env0_out_ent  env1_in_acc   env1_in_ent   env1_out_acc  env1_out_ent  env2_in_acc   env2_in_ent   env2_out_acc  env2_out_ent  env3_in_acc   env3_in_ent   env3_out_acc  env3_out_ent 
0.8541793777  0.1449299917  0.8361858191  0.1328322474  0.9989339019  0.0056033220  0.9444444444  0.0438111851  1.0000000000  0.0023905056  0.9760479042  0.0200748501  0.9939567430  0.0173334001  0.9579617834  0.0455281054 

After T3A
env0_in_acc   env0_in_ent   env0_out_acc  env0_out_ent  env1_in_acc   env1_in_ent   env1_out_acc  env1_out_ent  env2_in_acc   env2_in_ent   env2_out_acc  env2_out_ent  env3_in_acc   env3_in_ent   env3_out_acc  env3_out_ent  filter_K     
0.8615196078  0.0300979523  0.8463541667  0.0195607367  0.9983836207  0.0008239143  0.9665178571  0.0048839899  0.9992378049  0.0000014360  0.9687500000  0.0079756258  0.9926658163  0.0040930418  0.9531250000  0.0098382344  1            
0.8639705882  0.0303275467  0.8515625000  0.0209141834  0.9978448276  0.0007431315  0.9620535714  0.0076167837  0.9992378049  0.0000021623  0.9687500000  0.0054733354  0.9942602041  0.0031892638  0.9518229167  0.0104223108  5            
0.8676470588  0.0291756524  0.8515625000  0.0168399035  0.9978448276  0.0011644578  0.9620535714  0.0065066690  0.9992378049  0.0000008054  0.9687500000  0.0074157378  0.9933035714  0.0033154711  0.9505208333  0.0109221917  20           
0.8762254902  0.0294459265  0.8437500000  0.0226431394  0.9983836207  0.0012714628  0.9575892857  0.0074657881  0.9992378049  0.0000007235  0.9718750000  0.0074016270  0.9942602041  0.0030418858  0.9505208333  0.0100194169  50           
0.8768382353  0.0323306602  0.8437500000  0.0212254503  0.9989224138  0.0011713412  0.9575892857  0.0089458891  0.9992378049  0.0000009033  0.9718750000  0.0074016274  0.9955357143  0.0026035005  0.9505208333  0.0110478771  100          
0.8713235294  0.0330403700  0.8489583333  0.0175633694  0.9989224138  0.0014108214  0.9642857143  0.0100299865  0.9992378049  0.0000015491  0.9718750000  0.0060503826  0.9875637755  0.0039479561  0.9466145833  0.0148385064  -1           
