Precision: [tensor(0.5450, device='cuda:0'), tensor(0.5524, device='cuda:0'), tensor(0.5284, device='cuda:0'), tensor(0.5552, device='cuda:0'), tensor(0.5493, device='cuda:0'), tensor(0.5367, device='cuda:0'), tensor(0.5422, device='cuda:0'), tensor(0.5360, device='cuda:0'), tensor(0.5320, device='cuda:0'), tensor(0.5511, device='cuda:0')]

Output distance: [tensor(18.9353, device='cuda:0'), tensor(18.9205, device='cuda:0'), tensor(18.9686, device='cuda:0'), tensor(18.9151, device='cuda:0'), tensor(18.9268, device='cuda:0'), tensor(18.9519, device='cuda:0'), tensor(18.9411, device='cuda:0'), tensor(18.9534, device='cuda:0'), tensor(18.9613, device='cuda:0'), tensor(18.9232, device='cuda:0')]

Prediction loss: [tensor(108.8734, device='cuda:0'), tensor(108.5195, device='cuda:0'), tensor(107.8891, device='cuda:0'), tensor(108.5559, device='cuda:0'), tensor(108.9729, device='cuda:0'), tensor(108.8404, device='cuda:0'), tensor(109.3772, device='cuda:0'), tensor(108.7820, device='cuda:0'), tensor(109.2890, device='cuda:0'), tensor(109.0553, device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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=411498), datetime.timedelta(seconds=2, microseconds=365043), datetime.timedelta(seconds=2, microseconds=588102), datetime.timedelta(seconds=2, microseconds=576154), datetime.timedelta(seconds=2, microseconds=426782), datetime.timedelta(seconds=2, microseconds=456654), datetime.timedelta(seconds=2, microseconds=488523), datetime.timedelta(seconds=2, microseconds=471595), datetime.timedelta(seconds=2, microseconds=440725), datetime.timedelta(seconds=2, microseconds=440723)]

Phi time: [datetime.timedelta(seconds=4, microseconds=948021), datetime.timedelta(seconds=5, microseconds=31006), datetime.timedelta(seconds=5, microseconds=233412), datetime.timedelta(seconds=5, microseconds=132181), datetime.timedelta(seconds=5, microseconds=192701), datetime.timedelta(seconds=5, microseconds=151083), datetime.timedelta(seconds=5, microseconds=182145), datetime.timedelta(seconds=5, microseconds=184709), datetime.timedelta(seconds=5, microseconds=129913), datetime.timedelta(seconds=5, microseconds=126119)]

