Precision: [tensor(0.5162, device='cuda:0'), tensor(0.5284, device='cuda:0'), tensor(0.5278, device='cuda:0'), tensor(0.5233, device='cuda:0'), tensor(0.5148, device='cuda:0'), tensor(0.5316, device='cuda:0'), tensor(0.5165, device='cuda:0'), tensor(0.5204, device='cuda:0'), tensor(0.5218, device='cuda:0'), tensor(0.5204, device='cuda:0')]

Output distance: [tensor(18.9930, device='cuda:0'), tensor(18.9686, device='cuda:0'), tensor(18.9698, device='cuda:0'), tensor(18.9788, device='cuda:0'), tensor(18.9958, device='cuda:0'), tensor(18.9622, device='cuda:0'), tensor(18.9924, device='cuda:0'), tensor(18.9846, device='cuda:0'), tensor(18.9819, device='cuda:0'), tensor(18.9846, device='cuda:0')]

Prediction loss: [tensor(108.5905, device='cuda:0'), tensor(107.5860, device='cuda:0'), tensor(108.1633, device='cuda:0'), tensor(109.0776, device='cuda:0'), tensor(109.6547, device='cuda:0'), tensor(108.4777, device='cuda:0'), tensor(108.1339, device='cuda:0'), tensor(109.1545, device='cuda:0'), tensor(108.7101, device='cuda:0'), tensor(109.1730, 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': 7, '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=561137), datetime.timedelta(seconds=2, microseconds=568108), datetime.timedelta(seconds=2, microseconds=612918), datetime.timedelta(seconds=2, microseconds=634821), datetime.timedelta(seconds=2, microseconds=650760), datetime.timedelta(seconds=2, microseconds=773240), datetime.timedelta(seconds=2, microseconds=638807), datetime.timedelta(seconds=2, microseconds=604952), datetime.timedelta(seconds=2, microseconds=619891), datetime.timedelta(seconds=2, microseconds=628849)]

Phi time: [datetime.timedelta(seconds=5, microseconds=262343), datetime.timedelta(seconds=5, microseconds=244638), datetime.timedelta(seconds=5, microseconds=256795), datetime.timedelta(seconds=5, microseconds=302871), datetime.timedelta(seconds=5, microseconds=411900), datetime.timedelta(seconds=5, microseconds=398014), datetime.timedelta(seconds=5, microseconds=432514), datetime.timedelta(seconds=5, microseconds=439016), datetime.timedelta(seconds=5, microseconds=414893), datetime.timedelta(seconds=5, microseconds=381916)]

