Precision: [tensor(0.1165, device='cuda:0'), tensor(0.1166, device='cuda:0'), tensor(0.1135, device='cuda:0'), tensor(0.1171, device='cuda:0'), tensor(0.1277, device='cuda:0'), tensor(0.1196, device='cuda:0'), tensor(0.1257, device='cuda:0'), tensor(0.1155, device='cuda:0'), tensor(0.1177, device='cuda:0'), tensor(0.1114, device='cuda:0')]

Output distance: [tensor(22.8603, device='cuda:0'), tensor(22.8597, device='cuda:0'), tensor(22.8900, device='cuda:0'), tensor(22.8546, device='cuda:0'), tensor(22.7485, device='cuda:0'), tensor(22.8298, device='cuda:0'), tensor(22.7684, device='cuda:0'), tensor(22.8700, device='cuda:0'), tensor(22.8485, device='cuda:0'), tensor(22.9111, device='cuda:0')]

Prediction loss: [tensor(111.7237, device='cuda:0'), tensor(111.4516, device='cuda:0'), tensor(110.8909, device='cuda:0'), tensor(112.5933, device='cuda:0'), tensor(110.9766, device='cuda:0'), tensor(111.8601, device='cuda:0'), tensor(110.4924, device='cuda:0'), tensor(112.8104, device='cuda:0'), tensor(111.5523, device='cuda:0'), tensor(111.2454, 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=15214), datetime.timedelta(seconds=7, microseconds=22215), datetime.timedelta(seconds=7, microseconds=46064), datetime.timedelta(seconds=7, microseconds=41137), datetime.timedelta(seconds=7, microseconds=118806), datetime.timedelta(seconds=7, microseconds=43128), datetime.timedelta(seconds=7, microseconds=57067), datetime.timedelta(seconds=7, microseconds=36159), datetime.timedelta(seconds=7, microseconds=37154), datetime.timedelta(seconds=7, microseconds=41137)]

Phi time: [datetime.timedelta(seconds=5, microseconds=82828), datetime.timedelta(seconds=5, microseconds=109762), datetime.timedelta(seconds=5, microseconds=159208), datetime.timedelta(seconds=5, microseconds=170143), datetime.timedelta(seconds=5, microseconds=147288), datetime.timedelta(seconds=5, microseconds=119612), datetime.timedelta(seconds=5, microseconds=148755), datetime.timedelta(seconds=5, microseconds=156699), datetime.timedelta(seconds=5, microseconds=167485), datetime.timedelta(seconds=5, microseconds=140545)]

