Precision: [tensor(0.6477, device='cuda:0'), tensor(0.6474, device='cuda:0'), tensor(0.6419, device='cuda:0'), tensor(0.6422, device='cuda:0'), tensor(0.6500, device='cuda:0'), tensor(0.6401, device='cuda:0'), tensor(0.6440, device='cuda:0'), tensor(0.6419, device='cuda:0'), tensor(0.6461, device='cuda:0'), tensor(0.6440, device='cuda:0')]

Output distance: [tensor(5.0108, device='cuda:0'), tensor(5.0113, device='cuda:0'), tensor(5.0223, device='cuda:0'), tensor(5.0218, device='cuda:0'), tensor(5.0060, device='cuda:0'), tensor(5.0260, device='cuda:0'), tensor(5.0181, device='cuda:0'), tensor(5.0223, device='cuda:0'), tensor(5.0139, device='cuda:0'), tensor(5.0181, device='cuda:0')]

Prediction loss: [tensor(17622092., device='cuda:0'), tensor(20576780., device='cuda:0'), tensor(17787924., device='cuda:0'), tensor(18966472., device='cuda:0'), tensor(16285071., device='cuda:0'), tensor(20434604., device='cuda:0'), tensor(15751991., device='cuda:0'), tensor(15831745., device='cuda:0'), tensor(20088590., device='cuda:0'), tensor(17647836., device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40855.1211, device='cuda:0'), tensor(40976.0312, device='cuda:0'), tensor(40834.2812, device='cuda:0'), tensor(40839.9648, device='cuda:0'), tensor(41144.1328, device='cuda:0'), tensor(40879.0898, device='cuda:0'), tensor(40918.7344, device='cuda:0'), tensor(40915.7578, device='cuda:0'), tensor(41138.1211, device='cuda:0'), tensor(41223.9766, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=13708), datetime.timedelta(microseconds=978434), datetime.timedelta(microseconds=971694), datetime.timedelta(microseconds=973878), datetime.timedelta(microseconds=973752), datetime.timedelta(microseconds=981498), datetime.timedelta(microseconds=986006), datetime.timedelta(microseconds=972762), datetime.timedelta(microseconds=966437), datetime.timedelta(microseconds=968427)]

Phi time: [datetime.timedelta(microseconds=166363), datetime.timedelta(microseconds=197196), datetime.timedelta(microseconds=197243), datetime.timedelta(microseconds=180813), datetime.timedelta(microseconds=178268), datetime.timedelta(microseconds=169273), datetime.timedelta(microseconds=195082), datetime.timedelta(microseconds=194805), datetime.timedelta(microseconds=194505), datetime.timedelta(microseconds=180469)]

