Precision: [tensor(0.5695, device='cuda:0'), tensor(0.5670, device='cuda:0'), tensor(0.5670, device='cuda:0'), tensor(0.5781, device='cuda:0'), tensor(0.5576, device='cuda:0'), tensor(0.5538, device='cuda:0'), tensor(0.5806, device='cuda:0'), tensor(0.5465, device='cuda:0'), tensor(0.5718, device='cuda:0'), tensor(0.5638, device='cuda:0')]
Output distance: [tensor(18.8998, device='cuda:0'), tensor(18.9048, device='cuda:0'), tensor(18.9042, device='cuda:0'), tensor(18.8844, device='cuda:0'), tensor(18.9214, device='cuda:0'), tensor(18.9284, device='cuda:0'), tensor(18.8801, device='cuda:0'), tensor(18.9418, device='cuda:0'), tensor(18.8959, device='cuda:0'), tensor(18.9105, device='cuda:0')]
Prediction loss: [tensor(109.1089, device='cuda:0'), tensor(109.4950, device='cuda:0'), tensor(108.8820, device='cuda:0'), tensor(109.2606, device='cuda:0'), tensor(108.3297, device='cuda:0'), tensor(108.3977, device='cuda:0'), tensor(108.8392, device='cuda:0'), tensor(108.3708, device='cuda:0'), tensor(109.4695, device='cuda:0'), tensor(108.2205, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5975, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5958, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5986, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5973, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5974, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5962, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5965, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5947, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5965, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5960, 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=2, microseconds=682935), datetime.timedelta(seconds=2, microseconds=699962), datetime.timedelta(seconds=2, microseconds=700085), datetime.timedelta(seconds=2, microseconds=683505), datetime.timedelta(seconds=2, microseconds=699625), datetime.timedelta(seconds=2, microseconds=683324), datetime.timedelta(seconds=2, microseconds=699965), datetime.timedelta(seconds=2, microseconds=721835), datetime.timedelta(seconds=2, microseconds=699937), datetime.timedelta(seconds=2, microseconds=701602)]
Phi time: [datetime.timedelta(seconds=99, microseconds=466357), datetime.timedelta(seconds=99, microseconds=336305), datetime.timedelta(seconds=99, microseconds=569807), datetime.timedelta(seconds=99, microseconds=518401), datetime.timedelta(seconds=99, microseconds=349500), datetime.timedelta(seconds=99, microseconds=532705), datetime.timedelta(seconds=99, microseconds=299348), datetime.timedelta(seconds=99, microseconds=466099), datetime.timedelta(seconds=99, microseconds=315074), datetime.timedelta(seconds=99, microseconds=399361)]
