Precision: [tensor(0.4832, device='cuda:0'), tensor(0.4814, device='cuda:0'), tensor(0.4882, device='cuda:0'), tensor(0.4661, device='cuda:0'), tensor(0.4630, device='cuda:0'), tensor(0.4628, device='cuda:0'), tensor(0.4825, device='cuda:0'), tensor(0.4498, device='cuda:0'), tensor(0.4946, device='cuda:0'), tensor(0.4770, device='cuda:0')]

Output distance: [tensor(19.0589, device='cuda:0'), tensor(19.0626, device='cuda:0'), tensor(19.0490, device='cuda:0'), tensor(19.0931, device='cuda:0'), tensor(19.0995, device='cuda:0'), tensor(19.0998, device='cuda:0'), tensor(19.0605, device='cuda:0'), tensor(19.1258, device='cuda:0'), tensor(19.0363, device='cuda:0'), tensor(19.0713, device='cuda:0')]

Prediction loss: [tensor(108.2449, device='cuda:0'), tensor(107.0129, device='cuda:0'), tensor(108.7340, device='cuda:0'), tensor(107.3316, device='cuda:0'), tensor(108.6900, device='cuda:0'), tensor(108.4250, device='cuda:0'), tensor(108.3680, device='cuda:0'), tensor(107.9386, device='cuda:0'), tensor(109.0054, device='cuda:0'), tensor(108.8283, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, '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=498308), datetime.timedelta(seconds=2, microseconds=499938), datetime.timedelta(seconds=2, microseconds=487087), datetime.timedelta(seconds=2, microseconds=497883), datetime.timedelta(seconds=2, microseconds=526907), datetime.timedelta(seconds=2, microseconds=533080), datetime.timedelta(seconds=2, microseconds=493342), datetime.timedelta(seconds=2, microseconds=572800), datetime.timedelta(seconds=2, microseconds=517511), datetime.timedelta(seconds=2, microseconds=492467)]

Phi time: [datetime.timedelta(seconds=4, microseconds=494334), datetime.timedelta(seconds=4, microseconds=467171), datetime.timedelta(seconds=4, microseconds=487382), datetime.timedelta(seconds=4, microseconds=525703), datetime.timedelta(seconds=4, microseconds=501315), datetime.timedelta(seconds=4, microseconds=492542), datetime.timedelta(seconds=4, microseconds=484603), datetime.timedelta(seconds=4, microseconds=547121), datetime.timedelta(seconds=4, microseconds=497725), datetime.timedelta(seconds=4, microseconds=444413)]

