Precision: [tensor(0.4415, device='cuda:0'), tensor(0.4373, device='cuda:0'), tensor(0.4441, device='cuda:0'), tensor(0.4383, device='cuda:0'), tensor(0.4510, device='cuda:0'), tensor(0.4492, device='cuda:0'), tensor(0.4407, device='cuda:0'), tensor(0.4265, device='cuda:0'), tensor(0.4510, device='cuda:0'), tensor(0.4371, device='cuda:0')]

Output distance: [tensor(19.1424, device='cuda:0'), tensor(19.1508, device='cuda:0'), tensor(19.1372, device='cuda:0'), tensor(19.1487, device='cuda:0'), tensor(19.1233, device='cuda:0'), tensor(19.1270, device='cuda:0'), tensor(19.1439, device='cuda:0'), tensor(19.1723, device='cuda:0'), tensor(19.1233, device='cuda:0'), tensor(19.1511, device='cuda:0')]

Prediction loss: [tensor(108.4878, device='cuda:0'), tensor(109.4486, device='cuda:0'), tensor(108.6079, device='cuda:0'), tensor(108.9952, device='cuda:0'), tensor(107.8250, device='cuda:0'), tensor(109.3273, device='cuda:0'), tensor(108.0128, device='cuda:0'), tensor(108.5073, device='cuda:0'), tensor(108.4800, device='cuda:0'), tensor(108.2728, 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=5, microseconds=407825), datetime.timedelta(seconds=5, microseconds=402848), datetime.timedelta(seconds=5, microseconds=411444), datetime.timedelta(seconds=5, microseconds=407559), datetime.timedelta(seconds=5, microseconds=475048), datetime.timedelta(seconds=5, microseconds=403491), datetime.timedelta(seconds=5, microseconds=423865), datetime.timedelta(seconds=5, microseconds=426620), datetime.timedelta(seconds=5, microseconds=430026), datetime.timedelta(seconds=5, microseconds=419624)]

Phi time: [datetime.timedelta(seconds=4, microseconds=318782), datetime.timedelta(seconds=4, microseconds=344470), datetime.timedelta(seconds=4, microseconds=362841), datetime.timedelta(seconds=4, microseconds=387883), datetime.timedelta(seconds=4, microseconds=349165), datetime.timedelta(seconds=4, microseconds=367992), datetime.timedelta(seconds=4, microseconds=349948), datetime.timedelta(seconds=4, microseconds=338774), datetime.timedelta(seconds=4, microseconds=399898), datetime.timedelta(seconds=4, microseconds=314435)]

