Precision: [tensor(0.4274, device='cuda:0'), tensor(0.4281, device='cuda:0'), tensor(0.4346, device='cuda:0'), tensor(0.4380, device='cuda:0'), tensor(0.4351, device='cuda:0'), tensor(0.4316, device='cuda:0'), tensor(0.4276, device='cuda:0'), tensor(0.4293, device='cuda:0'), tensor(0.4361, device='cuda:0'), tensor(0.4330, device='cuda:0')]
Output distance: [tensor(19.4607, device='cuda:0'), tensor(19.4568, device='cuda:0'), tensor(19.4181, device='cuda:0'), tensor(19.3975, device='cuda:0'), tensor(19.4148, device='cuda:0'), tensor(19.4356, device='cuda:0'), tensor(19.4598, device='cuda:0'), tensor(19.4495, device='cuda:0'), tensor(19.4087, device='cuda:0'), tensor(19.4275, device='cuda:0')]
Prediction loss: [tensor(104.5821, device='cuda:0'), tensor(105.1437, device='cuda:0'), tensor(105.2473, device='cuda:0'), tensor(104.5530, device='cuda:0'), tensor(104.9450, device='cuda:0'), tensor(105.0549, device='cuda:0'), tensor(103.9151, device='cuda:0'), tensor(104.4200, device='cuda:0'), tensor(104.4449, device='cuda:0'), tensor(104.6195, 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: [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=4, microseconds=419355), datetime.timedelta(seconds=4, microseconds=407336), datetime.timedelta(seconds=4, microseconds=406236), datetime.timedelta(seconds=4, microseconds=436447), datetime.timedelta(seconds=4, microseconds=424029), datetime.timedelta(seconds=4, microseconds=422977), datetime.timedelta(seconds=4, microseconds=412356), datetime.timedelta(seconds=4, microseconds=452149), datetime.timedelta(seconds=4, microseconds=429592), datetime.timedelta(seconds=4, microseconds=421447)]
Phi time: [datetime.timedelta(seconds=97, microseconds=671413), datetime.timedelta(seconds=97, microseconds=610164), datetime.timedelta(seconds=97, microseconds=391014), datetime.timedelta(seconds=97, microseconds=672356), datetime.timedelta(seconds=97, microseconds=484888), datetime.timedelta(seconds=97, microseconds=652723), datetime.timedelta(seconds=97, microseconds=416992), datetime.timedelta(seconds=97, microseconds=394480), datetime.timedelta(seconds=97, microseconds=489501), datetime.timedelta(seconds=97, microseconds=346196)]
