Precision: [tensor(0.4752, device='cuda:0'), tensor(0.4775, device='cuda:0'), tensor(0.4751, device='cuda:0'), tensor(0.4946, device='cuda:0'), tensor(0.4713, device='cuda:0'), tensor(0.4586, device='cuda:0'), tensor(0.4962, device='cuda:0'), tensor(0.4811, device='cuda:0'), tensor(0.4894, device='cuda:0'), tensor(0.4831, device='cuda:0')]

Output distance: [tensor(19.0750, device='cuda:0'), tensor(19.0704, device='cuda:0'), tensor(19.0753, device='cuda:0'), tensor(19.0363, device='cuda:0'), tensor(19.0828, device='cuda:0'), tensor(19.1082, device='cuda:0'), tensor(19.0329, device='cuda:0'), tensor(19.0632, device='cuda:0'), tensor(19.0466, device='cuda:0'), tensor(19.0592, device='cuda:0')]

Prediction loss: [tensor(108.8596, device='cuda:0'), tensor(108.0733, device='cuda:0'), tensor(108.3446, device='cuda:0'), tensor(110.0563, device='cuda:0'), tensor(108.4366, device='cuda:0'), tensor(108.8492, device='cuda:0'), tensor(108.4178, device='cuda:0'), tensor(108.6802, device='cuda:0'), tensor(108.6128, device='cuda:0'), tensor(109.0777, device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, 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=2, microseconds=223484), datetime.timedelta(seconds=2, microseconds=216465), datetime.timedelta(seconds=2, microseconds=209363), datetime.timedelta(seconds=2, microseconds=231995), datetime.timedelta(seconds=2, microseconds=221608), datetime.timedelta(seconds=2, microseconds=225721), datetime.timedelta(seconds=2, microseconds=224231), datetime.timedelta(seconds=2, microseconds=209648), datetime.timedelta(seconds=2, microseconds=252812), datetime.timedelta(seconds=2, microseconds=228795)]

Phi time: [datetime.timedelta(seconds=4, microseconds=455886), datetime.timedelta(seconds=4, microseconds=446292), datetime.timedelta(seconds=4, microseconds=399940), datetime.timedelta(seconds=4, microseconds=467739), datetime.timedelta(seconds=4, microseconds=458809), datetime.timedelta(seconds=4, microseconds=449289), datetime.timedelta(seconds=4, microseconds=429326), datetime.timedelta(seconds=4, microseconds=449841), datetime.timedelta(seconds=4, microseconds=423668), datetime.timedelta(seconds=4, microseconds=407580)]

