Precision: [tensor(0.5555, device='cuda:0'), tensor(0.5582, device='cuda:0'), tensor(0.5417, device='cuda:0'), tensor(0.5399, device='cuda:0'), tensor(0.5278, device='cuda:0'), tensor(0.5488, device='cuda:0'), tensor(0.5446, device='cuda:0'), tensor(0.5541, device='cuda:0'), tensor(0.5382, device='cuda:0'), tensor(0.5534, device='cuda:0')]
Output distance: [tensor(18.9145, device='cuda:0'), tensor(18.9090, device='cuda:0'), tensor(18.9420, device='cuda:0'), tensor(18.9456, device='cuda:0'), tensor(18.9698, device='cuda:0'), tensor(18.9278, device='cuda:0'), tensor(18.9362, device='cuda:0'), tensor(18.9172, device='cuda:0'), tensor(18.9489, device='cuda:0'), tensor(18.9187, device='cuda:0')]
Prediction loss: [tensor(109.8294, device='cuda:0'), tensor(108.7537, device='cuda:0'), tensor(108.9327, device='cuda:0'), tensor(108.9202, device='cuda:0'), tensor(109.4900, device='cuda:0'), tensor(108.5201, device='cuda:0'), tensor(108.6204, device='cuda:0'), tensor(108.8882, device='cuda:0'), tensor(108.9641, device='cuda:0'), tensor(109.5193, device='cuda:0')]
Others: [{'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, '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': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]
Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=4, microseconds=586550), datetime.timedelta(seconds=4, microseconds=177026), datetime.timedelta(seconds=4, microseconds=214231), datetime.timedelta(seconds=2, microseconds=530901), datetime.timedelta(seconds=4, microseconds=190948), datetime.timedelta(seconds=4, microseconds=189749), datetime.timedelta(seconds=4, microseconds=197693), datetime.timedelta(seconds=4, microseconds=185083), datetime.timedelta(seconds=4, microseconds=190907), datetime.timedelta(seconds=4, microseconds=180413)]
Phi time: [datetime.timedelta(seconds=121, microseconds=746811), datetime.timedelta(seconds=97, microseconds=593985), datetime.timedelta(seconds=96, microseconds=962315), datetime.timedelta(seconds=97, microseconds=221712), datetime.timedelta(seconds=97, microseconds=353302), datetime.timedelta(seconds=98, microseconds=555603), datetime.timedelta(seconds=97, microseconds=442730), datetime.timedelta(seconds=97, microseconds=426201), datetime.timedelta(seconds=97, microseconds=377751), datetime.timedelta(seconds=97, microseconds=315844)]
