Precision: [tensor(0.5429, device='cuda:0'), tensor(0.5594, device='cuda:0'), tensor(0.5410, device='cuda:0'), tensor(0.5627, device='cuda:0'), tensor(0.5325, device='cuda:0'), tensor(0.5526, device='cuda:0'), tensor(0.5482, device='cuda:0'), tensor(0.5503, device='cuda:0'), tensor(0.5618, device='cuda:0'), tensor(0.5378, device='cuda:0')]

Output distance: [tensor(18.9395, device='cuda:0'), tensor(18.9066, device='cuda:0'), tensor(18.9435, device='cuda:0'), tensor(18.8999, device='cuda:0'), tensor(18.9604, device='cuda:0'), tensor(18.9202, device='cuda:0'), tensor(18.9290, device='cuda:0'), tensor(18.9247, device='cuda:0'), tensor(18.9018, device='cuda:0'), tensor(18.9498, device='cuda:0')]

Prediction loss: [tensor(108.8862, device='cuda:0'), tensor(107.8063, device='cuda:0'), tensor(109.5300, device='cuda:0'), tensor(108.1248, device='cuda:0'), tensor(108.1401, device='cuda:0'), tensor(109.5967, device='cuda:0'), tensor(108.2771, device='cuda:0'), tensor(109.2256, device='cuda:0'), tensor(109.6816, device='cuda:0'), tensor(108.3766, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, '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=6, microseconds=968445), datetime.timedelta(seconds=6, microseconds=978403), datetime.timedelta(seconds=6, microseconds=973424), datetime.timedelta(seconds=6, microseconds=982383), datetime.timedelta(seconds=6, microseconds=988359), datetime.timedelta(seconds=6, microseconds=979399), datetime.timedelta(seconds=6, microseconds=972429), datetime.timedelta(seconds=7, microseconds=3297), datetime.timedelta(seconds=7, microseconds=19230), datetime.timedelta(seconds=6, microseconds=986366)]

Phi time: [datetime.timedelta(seconds=5, microseconds=79434), datetime.timedelta(seconds=5, microseconds=152746), datetime.timedelta(seconds=5, microseconds=144440), datetime.timedelta(seconds=5, microseconds=101745), datetime.timedelta(seconds=5, microseconds=154389), datetime.timedelta(seconds=5, microseconds=101855), datetime.timedelta(seconds=5, microseconds=154192), datetime.timedelta(seconds=5, microseconds=108832), datetime.timedelta(seconds=5, microseconds=141627), datetime.timedelta(seconds=5, microseconds=168259)]

