Precision: [tensor(0.2213, device='cuda:0'), tensor(0.2372, device='cuda:0'), tensor(0.2386, device='cuda:0'), tensor(0.2253, device='cuda:0'), tensor(0.2213, device='cuda:0'), tensor(0.2377, device='cuda:0'), tensor(0.2230, device='cuda:0'), tensor(0.2286, device='cuda:0'), tensor(0.2211, device='cuda:0'), tensor(0.2244, device='cuda:0')]

Output distance: [tensor(20.6977, device='cuda:0'), tensor(20.6022, device='cuda:0'), tensor(20.5940, device='cuda:0'), tensor(20.6735, device='cuda:0'), tensor(20.6974, device='cuda:0'), tensor(20.5995, device='cuda:0'), tensor(20.6871, device='cuda:0'), tensor(20.6536, device='cuda:0'), tensor(20.6986, device='cuda:0'), tensor(20.6790, device='cuda:0')]

Prediction loss: [tensor(107.4083, device='cuda:0'), tensor(106.3319, device='cuda:0'), tensor(107.1058, device='cuda:0'), tensor(107.4657, device='cuda:0'), tensor(107.4224, device='cuda:0'), tensor(107.7414, device='cuda:0'), tensor(106.4490, device='cuda:0'), tensor(106.3281, device='cuda:0'), tensor(108.5007, device='cuda:0'), tensor(106.8890, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, 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=849966), datetime.timedelta(seconds=3, microseconds=849954), datetime.timedelta(seconds=3, microseconds=853454), datetime.timedelta(seconds=3, microseconds=853819), datetime.timedelta(seconds=3, microseconds=859731), datetime.timedelta(seconds=3, microseconds=866857), datetime.timedelta(seconds=3, microseconds=869090), datetime.timedelta(seconds=3, microseconds=868101), datetime.timedelta(seconds=3, microseconds=861609), datetime.timedelta(seconds=3, microseconds=869277)]

Phi time: [datetime.timedelta(seconds=4, microseconds=302270), datetime.timedelta(seconds=4, microseconds=432112), datetime.timedelta(seconds=4, microseconds=405299), datetime.timedelta(seconds=4, microseconds=402831), datetime.timedelta(seconds=4, microseconds=371150), datetime.timedelta(seconds=4, microseconds=399921), datetime.timedelta(seconds=4, microseconds=428340), datetime.timedelta(seconds=4, microseconds=374696), datetime.timedelta(seconds=4, microseconds=426783), datetime.timedelta(seconds=4, microseconds=432446)]

