Precision: [tensor(0.1210, device='cuda:0'), tensor(0.1211, device='cuda:0'), tensor(0.1161, device='cuda:0'), tensor(0.1236, device='cuda:0'), tensor(0.1176, device='cuda:0'), tensor(0.1201, device='cuda:0'), tensor(0.1227, device='cuda:0'), tensor(0.1252, device='cuda:0'), tensor(0.1077, device='cuda:0'), tensor(0.1218, device='cuda:0')]

Output distance: [tensor(21.2993, device='cuda:0'), tensor(21.2990, device='cuda:0'), tensor(21.3289, device='cuda:0'), tensor(21.2839, device='cuda:0'), tensor(21.3198, device='cuda:0'), tensor(21.3047, device='cuda:0'), tensor(21.2890, device='cuda:0'), tensor(21.2742, device='cuda:0'), tensor(21.3794, device='cuda:0'), tensor(21.2947, device='cuda:0')]

Prediction loss: [tensor(110.3414, device='cuda:0'), tensor(109.6071, device='cuda:0'), tensor(112.4931, device='cuda:0'), tensor(110.7561, device='cuda:0'), tensor(111.2986, device='cuda:0'), tensor(110.8312, device='cuda:0'), tensor(109.6273, device='cuda:0'), tensor(110.5629, device='cuda:0'), tensor(111.9586, device='cuda:0'), tensor(111.6233, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, 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=720735), datetime.timedelta(seconds=5, microseconds=731850), datetime.timedelta(seconds=5, microseconds=726863), datetime.timedelta(seconds=5, microseconds=705954), datetime.timedelta(seconds=5, microseconds=720891), datetime.timedelta(seconds=5, microseconds=740807), datetime.timedelta(seconds=5, microseconds=737820), datetime.timedelta(seconds=5, microseconds=749770), datetime.timedelta(seconds=5, microseconds=751759), datetime.timedelta(seconds=5, microseconds=734852)]

Phi time: [datetime.timedelta(seconds=4, microseconds=382519), datetime.timedelta(seconds=4, microseconds=451319), datetime.timedelta(seconds=4, microseconds=516508), datetime.timedelta(seconds=4, microseconds=442441), datetime.timedelta(seconds=4, microseconds=498342), datetime.timedelta(seconds=4, microseconds=449576), datetime.timedelta(seconds=4, microseconds=472167), datetime.timedelta(seconds=4, microseconds=432878), datetime.timedelta(seconds=4, microseconds=475161), datetime.timedelta(seconds=4, microseconds=495635)]

