Precision: [tensor(0.2363, device='cuda:0'), tensor(0.2283, device='cuda:0'), tensor(0.2346, device='cuda:0'), tensor(0.2270, device='cuda:0'), tensor(0.2315, device='cuda:0'), tensor(0.2391, device='cuda:0'), tensor(0.2391, device='cuda:0'), tensor(0.2391, device='cuda:0'), tensor(0.2378, device='cuda:0'), tensor(0.2398, device='cuda:0')]

Output distance: [tensor(21.6620, device='cuda:0'), tensor(21.7427, device='cuda:0'), tensor(21.6790, device='cuda:0'), tensor(21.7557, device='cuda:0'), tensor(21.7101, device='cuda:0'), tensor(21.6348, device='cuda:0'), tensor(21.6348, device='cuda:0'), tensor(21.6339, device='cuda:0'), tensor(21.6469, device='cuda:0'), tensor(21.6279, device='cuda:0')]

Prediction loss: [tensor(99.0829, device='cuda:0'), tensor(99.2255, device='cuda:0'), tensor(99.5883, device='cuda:0'), tensor(99.2570, device='cuda:0'), tensor(99.0508, device='cuda:0'), tensor(100.1761, device='cuda:0'), tensor(99.7358, device='cuda:0'), tensor(101.2255, device='cuda:0'), tensor(100.1927, device='cuda:0'), tensor(100.1031, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(33080, 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=3, microseconds=102969), datetime.timedelta(seconds=3, microseconds=116911), datetime.timedelta(seconds=2, microseconds=980484), datetime.timedelta(seconds=3, microseconds=220473), datetime.timedelta(seconds=3, microseconds=76082), datetime.timedelta(seconds=3, microseconds=37243), datetime.timedelta(seconds=2, microseconds=978492), datetime.timedelta(seconds=3, microseconds=108947), datetime.timedelta(seconds=3, microseconds=93014), datetime.timedelta(seconds=3, microseconds=172594)]

Phi time: [datetime.timedelta(seconds=5, microseconds=136431), datetime.timedelta(seconds=5, microseconds=99585), datetime.timedelta(seconds=5, microseconds=62740), datetime.timedelta(seconds=5, microseconds=81145), datetime.timedelta(seconds=5, microseconds=128589), datetime.timedelta(seconds=5, microseconds=88222), datetime.timedelta(seconds=5, microseconds=94605), datetime.timedelta(seconds=5, microseconds=82656), datetime.timedelta(seconds=5, microseconds=125476), datetime.timedelta(seconds=5, microseconds=75590)]

