Repository: facebookresearch/DomainBed
Issue: About the DANN result
Root cause: Configuration Error

when we set 'algorithm=DANN', and run the code directly, the results in the paper are not available. Here is the output of the program run. why? Thanks.
Args:
	algorithm: DANN
	checkpoint_freq: None
	data_dir: /dataset/DG/
	dataset: PACS
	holdout_fraction: 0.2
	hparams: None
	hparams_seed: 0
	output_dir: train_output
	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:
	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: False
	resnet_dropout: 0.0
	weight_decay: 0.0
	weight_decay_d: 0.0
	weight_decay_g: 0.0
env0_in_acc   env0_out_acc  env1_in_acc   env1_out_acc  env2_in_acc   env2_out_acc  env3_in_acc   env3_out_acc  epoch         gen_loss      mem_gb        step          step_time    
0.2092739475  0.1931540342  0.2953091684  0.3119658120  0.3083832335  0.2724550898  0.2414122137  0.2242038217  0.0000000000  0.8979552984  7.9268550873  0             0.7343387604 
disc_loss     env0_in_acc   env0_out_acc  env1_in_acc   env1_out_acc  env2_in_acc   env2_out_acc  env3_in_acc   env3_out_acc  epoch         gen_loss      mem_gb        step          step_time    
1.2359811942  0.8078096400  0.7652811736  0.8528784648  0.8482905983  0.9812874251  0.9580838323  0.8374681934  0.8382165605  7.1856287425  -0.685258171  8.2017307281  300           0.4992028658 
11.189239563  0.4655277608  0.4621026895  0.7425373134  0.7371794872  0.7559880240  0.7335329341  0.7468193384  0.7414012739  14.371257485  3.4806638861  8.2017307281  600           0.4944124389 
238.60761779  0.1061622941  0.1442542787  0.2739872068  0.3055555556  0.1474550898  0.1886227545  0.2512722646  0.2369426752  21.556886227  5.7296134837  8.2017307281  900           0.4963528244 
2082.6883511  0.2245271507  0.2371638142  0.3187633262  0.3290598291  0.5022455090  0.4760479042  0.3985368957  0.3885350318  28.742514970  873.57823590  8.2017307281  1200          0.4959800331 
1.0357982743  0.2556436852  0.2567237164  0.3928571429  0.3696581197  0.5381736527  0.4940119760  0.4109414758  0.4152866242  35.928143712  3.9007051329  8.2017307281  1500          0.4922414263 
24.230077548  0.4002440513  0.3936430318  0.5986140725  0.5854700855  0.7020958084  0.6437125749  0.5540712468  0.5528662420  43.113772455  569.27031482  8.2017307281  1800          0.4939069223 
1.1945504181  0.3837705918  0.3911980440  0.5772921109  0.5641025641  0.7215568862  0.6706586826  0.6141857506  0.6394904459  50.299401197  1.0432499917  8.2017307281  2100          0.5033365639 
382.80088246  0.4051250763  0.4034229829  0.6753731343  0.6752136752  0.8203592814  0.7754491018  0.7019720102  0.7082802548  57.485029940  392.48228594  8.2017307281  2400          0.4943927431 
429.56468669  0.3715680293  0.3716381418  0.6988272921  0.7179487179  0.8248502994  0.7485029940  0.7659033079  0.7605095541  64.670658682  2884.0271361  8.2017307281  2700          0.4916381876 
1.2151116145  0.4130567419  0.4107579462  0.6023454158  0.6047008547  0.7754491018  0.7365269461  0.6669847328  0.6433121019  71.856287425  0.6902019918  8.2017307281  3000          0.4960203767 
244.13614944  0.3343502135  0.3496332518  0.6476545842  0.6517094017  0.7133233533  0.6826347305  0.7045165394  0.6917197452  79.041916167  314.87799603  8.2017307281  3300          0.4902488399 
70.464921302  0.5009151922  0.4938875306  0.6087420043  0.5940170940  0.7717065868  0.7664670659  0.6428117048  0.6471337580  86.227544910  170.52687933  8.2017307281  3600          0.4870569730 
2427.8547357  0.1696156193  0.1809290954  0.3678038380  0.3632478632  0.3016467066  0.2814371257  0.4736005089  0.4840764331  93.413173652  1438.1708783  8.2017307281  3900          0.4942047254 
890.91614838  0.1848688225  0.1882640587  0.4024520256  0.4273504274  0.5254491018  0.4880239521  0.3555979644  0.3350318471  100.59880239  1838.5080866  8.2017307281  4200          0.4956636135 
9005.7036366  0.3123856010  0.2787286064  0.4205756930  0.4059829060  0.5853293413  0.5658682635  0.3619592875  0.3656050955  107.78443113  1594.6786078  8.2017307281  4500          0.4901909248 
10238.970430  0.2733374009  0.2591687042  0.5042643923  0.4935897436  0.5209580838  0.4670658683  0.5063613232  0.4777070064  114.97005988  12094.406260  8.2017307281  4800          0.4918605773 
385962.36794  0.1928004881  0.2542787286  0.3155650320  0.3354700855  0.3031437126  0.3353293413  0.3005725191  0.3095541401  119.76047904  111583.26512  8.2017307281  5000          0.4945144749 

The results in the paper come from a full sweep of runs, and a subsequent model selection.

Full logs of the sweep: https://drive.google.com/file/d/16VFQWTble6-nB5AdXBtQpQFwjEC7CChM/view?usp=sharing