Precision: [tensor(0.5500, device='cuda:0'), tensor(0.5503, device='cuda:0'), tensor(0.5289, device='cuda:0'), tensor(0.5559, device='cuda:0'), tensor(0.5475, device='cuda:0'), tensor(0.5558, device='cuda:0'), tensor(0.5520, device='cuda:0'), tensor(0.5435, device='cuda:0'), tensor(0.5469, device='cuda:0'), tensor(0.5444, device='cuda:0')]
Output distance: [tensor(18.9253, device='cuda:0'), tensor(18.9247, device='cuda:0'), tensor(18.9677, device='cuda:0'), tensor(18.9135, device='cuda:0'), tensor(18.9305, device='cuda:0'), tensor(18.9138, device='cuda:0'), tensor(18.9214, device='cuda:0'), tensor(18.9383, device='cuda:0'), tensor(18.9317, device='cuda:0'), tensor(18.9365, device='cuda:0')]
Prediction loss: [tensor(108.7231, device='cuda:0'), tensor(108.2055, device='cuda:0'), tensor(108.6425, device='cuda:0'), tensor(109.3017, device='cuda:0'), tensor(109.0083, device='cuda:0'), tensor(109.0797, device='cuda:0'), tensor(108.8544, device='cuda:0'), tensor(108.7646, device='cuda:0'), tensor(108.5212, device='cuda:0'), tensor(108.8983, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]
Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=2, microseconds=671841), datetime.timedelta(seconds=2, microseconds=640314), datetime.timedelta(seconds=2, microseconds=671740), datetime.timedelta(seconds=2, microseconds=641599), datetime.timedelta(seconds=2, microseconds=655132), datetime.timedelta(seconds=2, microseconds=646034), datetime.timedelta(seconds=2, microseconds=647454), datetime.timedelta(seconds=2, microseconds=625958), datetime.timedelta(seconds=2, microseconds=659812), datetime.timedelta(seconds=2, microseconds=662525)]
Phi time: [datetime.timedelta(seconds=97, microseconds=593300), datetime.timedelta(seconds=97, microseconds=452996), datetime.timedelta(seconds=97, microseconds=515093), datetime.timedelta(seconds=97, microseconds=390120), datetime.timedelta(seconds=97, microseconds=593315), datetime.timedelta(seconds=97, microseconds=484886), datetime.timedelta(seconds=97, microseconds=470360), datetime.timedelta(seconds=97, microseconds=442033), datetime.timedelta(seconds=97, microseconds=627220), datetime.timedelta(seconds=97, microseconds=338523)]
