Precision: [tensor(0.7937, device='cuda:0'), tensor(0.8144, device='cuda:0'), tensor(0.8138, device='cuda:0'), tensor(0.8022, device='cuda:0'), tensor(0.8050, device='cuda:0'), tensor(0.7621, device='cuda:0'), tensor(0.8076, device='cuda:0'), tensor(0.7995, device='cuda:0'), tensor(0.8038, device='cuda:0'), tensor(0.8102, device='cuda:0')]

Output distance: [tensor(16551.8203, device='cuda:0'), tensor(14531.6475, device='cuda:0'), tensor(14683.9482, device='cuda:0'), tensor(15234.3076, device='cuda:0'), tensor(18697.3652, device='cuda:0'), tensor(120537.8516, device='cuda:0'), tensor(15652.9053, device='cuda:0'), tensor(16071.3350, device='cuda:0'), tensor(16868.2559, device='cuda:0'), tensor(15017.7744, device='cuda:0')]

Prediction loss: [tensor(11895.9277, device='cuda:0'), tensor(10736.3818, device='cuda:0'), tensor(11054.1475, device='cuda:0'), tensor(9895.6904, device='cuda:0'), tensor(13517.6543, device='cuda:0'), tensor(204207.6406, device='cuda:0'), tensor(11457.2881, device='cuda:0'), tensor(10819.9609, device='cuda:0'), tensor(13855.1543, device='cuda:0'), tensor(11228.2744, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.8967e+08, device='cuda:0'), tensor(1.9649e+08, device='cuda:0'), tensor(2.0096e+08, device='cuda:0'), tensor(1.8219e+08, device='cuda:0'), tensor(1.8474e+08, device='cuda:0'), tensor(1.9982e+08, device='cuda:0'), tensor(1.8799e+08, device='cuda:0'), tensor(1.8770e+08, device='cuda:0'), tensor(2.0031e+08, device='cuda:0'), tensor(1.9966e+08, device='cuda:0')]

Training loss: 191842192.0

Prediction time: [datetime.timedelta(microseconds=983353), datetime.timedelta(seconds=1, microseconds=24229), datetime.timedelta(seconds=1, microseconds=12410), datetime.timedelta(seconds=1, microseconds=18903), datetime.timedelta(seconds=1, microseconds=12957), datetime.timedelta(seconds=1, microseconds=2061), datetime.timedelta(seconds=1, microseconds=22338), datetime.timedelta(seconds=1, microseconds=5166), datetime.timedelta(seconds=1, microseconds=10187), datetime.timedelta(seconds=1, microseconds=6174)]

Phi time: [datetime.timedelta(seconds=1, microseconds=77902), datetime.timedelta(microseconds=649942), datetime.timedelta(microseconds=566770), datetime.timedelta(microseconds=575187), datetime.timedelta(microseconds=559846), datetime.timedelta(microseconds=562861), datetime.timedelta(microseconds=560113), datetime.timedelta(microseconds=566994), datetime.timedelta(microseconds=568945), datetime.timedelta(microseconds=571940)]

