Precision: [tensor(0.7347, device='cuda:0'), tensor(0.7346, device='cuda:0'), tensor(0.7255, device='cuda:0'), tensor(0.7385, device='cuda:0'), tensor(0.7268, device='cuda:0'), tensor(0.7352, device='cuda:0'), tensor(0.7328, device='cuda:0'), tensor(0.7324, device='cuda:0'), tensor(0.7323, device='cuda:0'), tensor(0.7438, device='cuda:0')]
Output distance: [tensor(5.0116, device='cuda:0'), tensor(5.0129, device='cuda:0'), tensor(5.0223, device='cuda:0'), tensor(5.0039, device='cuda:0'), tensor(5.0197, device='cuda:0'), tensor(5.0123, device='cuda:0'), tensor(5.0160, device='cuda:0'), tensor(5.0158, device='cuda:0'), tensor(5.0123, device='cuda:0'), tensor(5.0003, device='cuda:0')]
Prediction loss: [tensor(17997522., device='cuda:0'), tensor(19014946., device='cuda:0'), tensor(18940522., device='cuda:0'), tensor(17697570., device='cuda:0'), tensor(19491450., device='cuda:0'), tensor(19037970., device='cuda:0'), tensor(17119940., device='cuda:0'), tensor(17651948., device='cuda:0'), tensor(18432072., device='cuda:0'), tensor(19680136., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(2390, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2381, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2397, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2413, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2405, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2379, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2373, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2380, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2409, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2389, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40873.8633, device='cuda:0'), tensor(40855.4297, device='cuda:0'), tensor(40876.5938, device='cuda:0'), tensor(40751.8594, device='cuda:0'), tensor(40931.4453, device='cuda:0'), tensor(40865.1250, device='cuda:0'), tensor(40847.5508, device='cuda:0'), tensor(40874.0664, device='cuda:0'), tensor(40916.7891, device='cuda:0'), tensor(40849.1914, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=86392), datetime.timedelta(seconds=1, microseconds=49548), datetime.timedelta(seconds=1, microseconds=48668), datetime.timedelta(seconds=1, microseconds=13701), datetime.timedelta(seconds=1, microseconds=14697), datetime.timedelta(seconds=1, microseconds=12706), datetime.timedelta(seconds=1, microseconds=168096), datetime.timedelta(seconds=1, microseconds=80465), datetime.timedelta(seconds=1, microseconds=123286), datetime.timedelta(seconds=1, microseconds=23671)]
Phi time: [datetime.timedelta(microseconds=238987), datetime.timedelta(microseconds=238989), datetime.timedelta(microseconds=236997), datetime.timedelta(microseconds=254924), datetime.timedelta(microseconds=255915), datetime.timedelta(microseconds=232070), datetime.timedelta(microseconds=229970), datetime.timedelta(microseconds=264824), datetime.timedelta(microseconds=260894), datetime.timedelta(microseconds=240973)]
