Precision: [tensor(0.3327, device='cuda:0'), tensor(0.3584, device='cuda:0'), tensor(0.3563, device='cuda:0'), tensor(0.3511, device='cuda:0'), tensor(0.3667, device='cuda:0'), tensor(0.3484, device='cuda:0'), tensor(0.3590, device='cuda:0'), tensor(0.3557, device='cuda:0'), tensor(0.3564, device='cuda:0'), tensor(0.3593, device='cuda:0')]

Output distance: [tensor(19.3600, device='cuda:0'), tensor(19.3086, device='cuda:0'), tensor(19.3129, device='cuda:0'), tensor(19.3232, device='cuda:0'), tensor(19.2920, device='cuda:0'), tensor(19.3286, device='cuda:0'), tensor(19.3074, device='cuda:0'), tensor(19.3141, device='cuda:0'), tensor(19.3126, device='cuda:0'), tensor(19.3068, device='cuda:0')]

Prediction loss: [tensor(107.0175, device='cuda:0'), tensor(108.0860, device='cuda:0'), tensor(108.2809, device='cuda:0'), tensor(108.9097, device='cuda:0'), tensor(107.6998, device='cuda:0'), tensor(106.4386, device='cuda:0'), tensor(110.8182, device='cuda:0'), tensor(107.6497, device='cuda:0'), tensor(107.2579, device='cuda:0'), tensor(107.2005, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=3, microseconds=665707), datetime.timedelta(seconds=3, microseconds=600106), datetime.timedelta(seconds=3, microseconds=628741), datetime.timedelta(seconds=3, microseconds=603025), datetime.timedelta(seconds=3, microseconds=605569), datetime.timedelta(seconds=3, microseconds=607841), datetime.timedelta(seconds=3, microseconds=600082), datetime.timedelta(seconds=3, microseconds=615705), datetime.timedelta(seconds=3, microseconds=637043), datetime.timedelta(seconds=3, microseconds=625223)]

Phi time: [datetime.timedelta(seconds=4, microseconds=214030), datetime.timedelta(seconds=4, microseconds=280929), datetime.timedelta(seconds=4, microseconds=274902), datetime.timedelta(seconds=4, microseconds=259705), datetime.timedelta(seconds=4, microseconds=265960), datetime.timedelta(seconds=4, microseconds=326199), datetime.timedelta(seconds=4, microseconds=281005), datetime.timedelta(seconds=4, microseconds=271494), datetime.timedelta(seconds=4, microseconds=329060), datetime.timedelta(seconds=4, microseconds=277140)]

