Precision: [tensor(0.2359, device='cuda:0'), tensor(0.2458, device='cuda:0'), tensor(0.2340, device='cuda:0'), tensor(0.2373, device='cuda:0'), tensor(0.2452, device='cuda:0'), tensor(0.2587, device='cuda:0'), tensor(0.2563, device='cuda:0'), tensor(0.2604, device='cuda:0'), tensor(0.2569, device='cuda:0'), tensor(0.2480, device='cuda:0')]

Output distance: [tensor(20.6097, device='cuda:0'), tensor(20.5505, device='cuda:0'), tensor(20.6215, device='cuda:0'), tensor(20.6016, device='cuda:0'), tensor(20.5541, device='cuda:0'), tensor(20.4731, device='cuda:0'), tensor(20.4873, device='cuda:0'), tensor(20.4631, device='cuda:0'), tensor(20.4840, device='cuda:0'), tensor(20.5375, device='cuda:0')]

Prediction loss: [tensor(101.5657, device='cuda:0'), tensor(103.2757, device='cuda:0'), tensor(102.2735, device='cuda:0'), tensor(103.0925, device='cuda:0'), tensor(101.6024, device='cuda:0'), tensor(103.7711, device='cuda:0'), tensor(102.9189, device='cuda:0'), tensor(103.8896, device='cuda:0'), tensor(103.4863, device='cuda:0'), tensor(102.7109, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, '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=2, microseconds=484317), datetime.timedelta(seconds=2, microseconds=408668), datetime.timedelta(seconds=2, microseconds=411842), datetime.timedelta(seconds=2, microseconds=400099), datetime.timedelta(seconds=2, microseconds=485931), datetime.timedelta(seconds=2, microseconds=435687), datetime.timedelta(seconds=2, microseconds=650759), datetime.timedelta(seconds=2, microseconds=530266), datetime.timedelta(seconds=2, microseconds=627856), datetime.timedelta(seconds=2, microseconds=720461)]

Phi time: [datetime.timedelta(seconds=4, microseconds=294957), datetime.timedelta(seconds=4, microseconds=263859), datetime.timedelta(seconds=4, microseconds=300005), datetime.timedelta(seconds=4, microseconds=279304), datetime.timedelta(seconds=4, microseconds=300050), datetime.timedelta(seconds=4, microseconds=295947), datetime.timedelta(seconds=4, microseconds=262992), datetime.timedelta(seconds=4, microseconds=555328), datetime.timedelta(seconds=4, microseconds=486467), datetime.timedelta(seconds=4, microseconds=458119)]

