Precision: [tensor(0.4386, device='cuda:0'), tensor(0.4354, device='cuda:0'), tensor(0.4380, device='cuda:0'), tensor(0.4382, device='cuda:0'), tensor(0.4284, device='cuda:0'), tensor(0.4260, device='cuda:0'), tensor(0.4381, device='cuda:0'), tensor(0.4335, device='cuda:0'), tensor(0.4363, device='cuda:0'), tensor(0.4304, device='cuda:0')]
Output distance: [tensor(19.3936, device='cuda:0'), tensor(19.4129, device='cuda:0'), tensor(19.3972, device='cuda:0'), tensor(19.3960, device='cuda:0'), tensor(19.4553, device='cuda:0'), tensor(19.4692, device='cuda:0'), tensor(19.3969, device='cuda:0'), tensor(19.4241, device='cuda:0'), tensor(19.4078, device='cuda:0'), tensor(19.4432, device='cuda:0')]
Prediction loss: [tensor(104.8391, device='cuda:0'), tensor(104.4542, device='cuda:0'), tensor(104.7489, device='cuda:0'), tensor(104.7363, device='cuda:0'), tensor(104.3122, device='cuda:0'), tensor(104.4612, device='cuda:0'), tensor(104.7510, device='cuda:0'), tensor(105.2603, device='cuda:0'), tensor(104.5954, device='cuda:0'), tensor(104.9087, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, '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=2, microseconds=749951), datetime.timedelta(seconds=2, microseconds=932072), datetime.timedelta(seconds=2, microseconds=783684), datetime.timedelta(seconds=2, microseconds=865031), datetime.timedelta(seconds=2, microseconds=750071), datetime.timedelta(seconds=2, microseconds=750068), datetime.timedelta(seconds=2, microseconds=883497), datetime.timedelta(seconds=2, microseconds=883223), datetime.timedelta(seconds=2, microseconds=897657), datetime.timedelta(seconds=2, microseconds=749855)]
Phi time: [datetime.timedelta(seconds=99, microseconds=188931), datetime.timedelta(seconds=99, microseconds=66141), datetime.timedelta(seconds=99, microseconds=65851), datetime.timedelta(seconds=98, microseconds=717687), datetime.timedelta(seconds=98, microseconds=699459), datetime.timedelta(seconds=98, microseconds=901782), datetime.timedelta(seconds=98, microseconds=802726), datetime.timedelta(seconds=98, microseconds=836451), datetime.timedelta(seconds=99, microseconds=171902), datetime.timedelta(seconds=99, microseconds=151753)]
