t1 = torch.tensor([0.0906, 0.1655, 0.0000, 0.3719, 0.0000, 0.0000, 0.4430, 0.0000, 0.3730,
0.1266, 0.0000, 0.6395, 0.0000, 0.1207, 0.4568, 0.0000, 0.6271, 0.0000,
0.0000, 0.7378, 0.0000, 0.3717, 0.2919, 0.0000, 0.8226, 0.0000, 0.0000,
0.6840, 0.0000, 0.6421, 0.0147, 0.0000, 0.9095, 0.0000, 0.2307, 0.4923,
0.0000, 0.8629, 0.0000, 0.0000, 0.8621, 0.0000, 0.5355, 0.1971, 0.0000,
0.9826, 0.0000, 0.0211, 0.6814, 0.0000, 0.7961, 0.0000, 0.0000, 0.9715,
0.0000, 0.3510, 0.3933, 0.0000, 0.9612, 0.0000, 0.0000, 0.8254, 0.0000,
0.6456, 0.0433, 0.0000, 0.9984, 0.0000, 0.1246, 0.5653, 0.0000, 0.8528,
0.0000, 0.0000, 0.8991, 0.0000, 0.4378, 0.2331, 0.0000, 0.9367, 0.0000,
0.0000, 0.6800, 0.0000, 0.6727, 0.0000, 0.0000, 0.8842, 0.0000, 0.2043,
0.3807, 0.0000, 0.7892, 0.0000, 0.0000, 0.7080, 0.0000, 0.4442, 0.0571,
0.0000, 0.7686, 0.0000, 0.0000, 0.4457, 0.0000, 0.5654, 0.0000, 0.0000,
0.6181, 0.0000, 0.1990, 0.1562, 0.0000, 0.5377, 0.0000, 0.0000, 0.3707,
0.0000, 0.2729, 0.0000, 0.0000, 0.3420, 0.0000, 0.0000, 0.0755, 0.0000,
0.0689, 0.0000])
t2 = torch.tensor([0.0000e+00, 0.0000e+00, 1.0001e-07, 1.4898e-34, 0.0000e+00, 1.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 8.2454e-35, 5.5626e-36, 3.1411e-37,
0.0000e+00, 5.0282e-37, 0.0000e+00, 0.0000e+00, 2.9625e-36, 4.6642e-33,
5.1396e-26, 0.0000e+00, 1.2186e-08, 6.8570e-37, 1.8719e-35, 0.0000e+00,
0.0000e+00, 8.0672e-16, 9.9961e-01, 5.7946e-19, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 1.1239e-29, 1.1849e-23, 8.2719e-32, 1.9147e-23,
0.0000e+00, 3.9956e-16, 0.0000e+00, 5.9800e-19, 0.0000e+00, 0.0000e+00,
0.0000e+00, 6.7426e-16, 0.0000e+00, 2.4604e-32, 6.8941e-32, 6.1563e-34,
0.0000e+00, 9.1442e-25, 0.0000e+00, 1.0000e+00, 6.3760e-21, 0.0000e+00,
0.0000e+00, 7.6713e-31, 0.0000e+00, 0.0000e+00, 0.0000e+00, 4.8445e-12,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.4843e-12, 0.0000e+00,
2.3431e-32, 9.9998e-01, 1.3083e-15, 0.0000e+00, 0.0000e+00, 0.0000e+00,
9.9988e-01, 0.0000e+00, 0.0000e+00, 4.5408e-27, 4.0411e-31, 0.0000e+00,
5.0567e-39, 0.0000e+00, 0.0000e+00, 5.6249e-09, 0.0000e+00, 4.4536e-16,
0.0000e+00, 9.9994e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 6.2092e-19,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.6951e-15, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9993e-01,
9.9991e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 6.4316e-14, 0.0000e+00,
0.0000e+00, 0.0000e+00, 4.3031e-26, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 3.6186e-08, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
2.1497e-17, 0.0000e+00, 2.2815e-09, 2.2894e-35, 8.5116e-30, 1.0000e+00,
4.4553e-29, 1.0000e+00])
norm = torch.nn.functional.normalize((t1*t2).unsqueeze(0), p=1.0, dim=1).unsqueeze(-1)
print(norm.sum())
The result is not reasonable to me, I am wondering if the wrong result is caused by tensor.float precision?