Precision: [tensor(0.4878, device='cuda:0'), tensor(0.4967, device='cuda:0'), tensor(0.4822, device='cuda:0'), tensor(0.4769, device='cuda:0'), tensor(0.4518, device='cuda:0'), tensor(0.4864, device='cuda:0'), tensor(0.4885, device='cuda:0'), tensor(0.4799, device='cuda:0'), tensor(0.4832, device='cuda:0'), tensor(0.4882, device='cuda:0')]

Output distance: [tensor(19.0499, device='cuda:0'), tensor(19.0320, device='cuda:0'), tensor(19.0611, device='cuda:0'), tensor(19.0716, device='cuda:0'), tensor(19.1218, device='cuda:0'), tensor(19.0526, device='cuda:0'), tensor(19.0484, device='cuda:0'), tensor(19.0656, device='cuda:0'), tensor(19.0589, device='cuda:0'), tensor(19.0490, device='cuda:0')]

Prediction loss: [tensor(109.5764, device='cuda:0'), tensor(109.5737, device='cuda:0'), tensor(107.8428, device='cuda:0'), tensor(108.3118, device='cuda:0'), tensor(108.5884, device='cuda:0'), tensor(108.1494, device='cuda:0'), tensor(108.6291, device='cuda:0'), tensor(108.5030, device='cuda:0'), tensor(109.1110, device='cuda:0'), tensor(108.3456, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, '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=5, microseconds=516697), datetime.timedelta(seconds=5, microseconds=493117), datetime.timedelta(seconds=5, microseconds=550087), datetime.timedelta(seconds=5, microseconds=519927), datetime.timedelta(seconds=5, microseconds=518408), datetime.timedelta(seconds=5, microseconds=521185), datetime.timedelta(seconds=5, microseconds=508054), datetime.timedelta(seconds=5, microseconds=502198), datetime.timedelta(seconds=5, microseconds=510161), datetime.timedelta(seconds=5, microseconds=506560)]

Phi time: [datetime.timedelta(seconds=4, microseconds=354561), datetime.timedelta(seconds=4, microseconds=454615), datetime.timedelta(seconds=4, microseconds=476594), datetime.timedelta(seconds=4, microseconds=409840), datetime.timedelta(seconds=4, microseconds=452134), datetime.timedelta(seconds=4, microseconds=419929), datetime.timedelta(seconds=4, microseconds=443831), datetime.timedelta(seconds=4, microseconds=476597), datetime.timedelta(seconds=4, microseconds=464872), datetime.timedelta(seconds=4, microseconds=442249)]

