Precision: [tensor(0.2445, device='cuda:0'), tensor(0.2376, device='cuda:0'), tensor(0.2462, device='cuda:0'), tensor(0.2478, device='cuda:0'), tensor(0.2375, device='cuda:0'), tensor(0.2474, device='cuda:0'), tensor(0.2388, device='cuda:0'), tensor(0.2320, device='cuda:0'), tensor(0.2437, device='cuda:0'), tensor(0.2434, device='cuda:0')]

Output distance: [tensor(21.5807, device='cuda:0'), tensor(21.6493, device='cuda:0'), tensor(21.5632, device='cuda:0'), tensor(21.5478, device='cuda:0'), tensor(21.6508, device='cuda:0'), tensor(21.5517, device='cuda:0'), tensor(21.6372, device='cuda:0'), tensor(21.7053, device='cuda:0'), tensor(21.5889, device='cuda:0'), tensor(21.5913, device='cuda:0')]

Prediction loss: [tensor(106.3274, device='cuda:0'), tensor(105.6060, device='cuda:0'), tensor(105.9764, device='cuda:0'), tensor(106.5114, device='cuda:0'), tensor(106.4573, device='cuda:0'), tensor(106.8307, device='cuda:0'), tensor(106.2439, device='cuda:0'), tensor(106.7610, device='cuda:0'), tensor(105.3072, device='cuda:0'), tensor(105.3707, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, 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=7, microseconds=639597), datetime.timedelta(seconds=7, microseconds=650550), datetime.timedelta(seconds=7, microseconds=665438), datetime.timedelta(seconds=7, microseconds=680484), datetime.timedelta(seconds=7, microseconds=857956), datetime.timedelta(seconds=7, microseconds=645794), datetime.timedelta(seconds=7, microseconds=661729), datetime.timedelta(seconds=7, microseconds=688617), datetime.timedelta(seconds=7, microseconds=692601), datetime.timedelta(seconds=7, microseconds=662723)]

Phi time: [datetime.timedelta(seconds=5, microseconds=304244), datetime.timedelta(seconds=5, microseconds=333252), datetime.timedelta(seconds=5, microseconds=358988), datetime.timedelta(seconds=5, microseconds=374522), datetime.timedelta(seconds=5, microseconds=436276), datetime.timedelta(seconds=5, microseconds=430252), datetime.timedelta(seconds=5, microseconds=383788), datetime.timedelta(seconds=5, microseconds=381280), datetime.timedelta(seconds=5, microseconds=393045), datetime.timedelta(seconds=5, microseconds=386711)]

