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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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trace, reciprocal and rsqrt in PyTorch

#python #pytorch #trace #reciprocal

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*Memos:

trace() can get the 0D tensor of the sum of the zero or more elements of diagonal from the 2D tensor of zero or more elements as shown below:

*Memos:

  • trace() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float or complex). 
import torch my_tensor = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) torch.trace(input=my_tensor) my_tensor.trace() # tensor(12)  my_tensor = torch.tensor([[0., 1., 2.], [3., 4., 5.], [6., 7., 8.]]) torch.trace(input=my_tensor) # tensor(12.)  my_tensor = torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j, 5.+0.j], [6.+0.j, 7.+0.j, 8.+0.j]]) torch.trace(input=my_tensor) # tensor(12.+0.j)  my_tensor = torch.tensor([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]) torch.trace(input=my_tensor) # tensor(15)  my_tensor = torch.tensor([[0, 1, 2], [3, 4, 5]]) torch.trace(input=my_tensor) # tensor(4)  my_tensor = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]) torch.trace(input=my_tensor) # tensor(12)  my_tensor = torch.tensor([[]]) torch.trace(input=my_tensor) # tensor(0.)
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reciprocal() can get the 0D or more D tensor of zero or more reciprocals from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

  • reciprocal() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument.
  • reciprocal() returns a float type tensor except when input or a tensor is a complex type tensor. 
import torch my_tensor = torch.tensor(-4.) torch.reciprocal(input=my_tensor) my_tensor.reciprocal() # tensor(-0.2500)  my_tensor = torch.tensor([-4., -3., -2., -1., 0., 1., 2., 3.]) torch.reciprocal(input=my_tensor) # tensor([-0.2500, -0.3333, -0.5000, -1.0000,  inf, 1.0000, 0.5000, 0.3333]) my_tensor = torch.tensor([[-4., -3., -2., -1.], [0., 1., 2., 3.]]) torch.reciprocal(input=my_tensor) # tensor([[-0.2500, -0.3333, -0.5000, -1.0000], # [inf, 1.0000, 0.5000, 0.3333]])  my_tensor = torch.tensor([[[-4., -3.], [-2., -1.]], [[0., 1.], [2., 3.]]]) torch.reciprocal(input=my_tensor) # tensor([[[-0.2500, -0.3333], [-0.5000, -1.0000]], # [[inf, 1.0000], [0.5000, 0.3333]]])  my_tensor = torch.tensor([[[-4, -3], [-2, -1]], [[0, 1], [2, 3]]]) torch.reciprocal(input=my_tensor) # tensor([[[-0.2500, -0.3333], [-0.5000, -1.0000]], # [[inf, 1.0000], [0.5000, 0.3333]]])  my_tensor = torch.tensor([[[-4.+0.j, -3.+0.j], [-2.+0.j, -1.+0.j]], [[0.+0.j, 1.+0.j], [2.+0.j, 3.+0.j]]]) torch.reciprocal(input=my_tensor) # tensor([[[-0.2500-0.j, -0.3333-0.j], [-0.5000-0.j, -1.0000-0.j]], # [[nan+nanj, 1.0000-0.j], [ 0.5000-0.j, 0.3333-0.j]]])  my_tensor = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]]) torch.reciprocal(input=my_tensor) # tensor([[[1., inf], [1., inf]], # [[inf, 1.], [inf, 1.]]])
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rsqrt() can get the 0D or more D tensor of the zero or more reciprocals of square root from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

  • rsqrt() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument.
  • rsqrt() returns a float type tensor except when input or a tensor is a complex type tensor. 
import torch my_tensor = torch.tensor(-3.) torch.rsqrt(input=my_tensor) my_tensor.rsqrt() # tensor(nan)  my_tensor = torch.tensor([-3., -2., -1., 0., 1., 2., 3., 4.]) torch.rsqrt(input=my_tensor) # tensor([nan, nan, nan, inf, 1.0000, 0.7071, 0.5774, 0.5000])  my_tensor = torch.tensor([[-3., -2., -1., 0.], [1., 2., 3., 4.]]) torch.rsqrt(input=my_tensor) # tensor([[nan, nan, nan, inf], # [1.0000, 0.7071, 0.5774, 0.5000]])  my_tensor = torch.tensor([[[-3., -2.], [-1., 0.]], [[1., 2.], [3., 4.]]]) torch.rsqrt(input=my_tensor) # tensor([[[nan, nan], # [nan, inf]], # [[1.0000, 0.7071], # [0.5774, 0.5000]]])  my_tensor = torch.tensor([[[-3, -2], [-1, 0]], [[1, 2], [3, 4]]]) torch.rsqrt(input=my_tensor) # tensor([[[nan, nan], # [nan, inf]], # [[1.0000, 0.7071], # [0.5774, 0.5000]]])  my_tensor = torch.tensor([[[-3.+0.j, -2.+0.j], [-1.+0.j, 0.+0.j]], [[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]]) torch.rsqrt(input=my_tensor) # tensor([[[0.0000-0.5774j, 0.0000-0.7071j], # [0.0000-1.0000j, nan+nanj]], # [[1.0000-0.0000j, 0.7071-0.0000j], # [0.5774-0.0000j, 0.5000-0.0000j]]])  my_tensor = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]]) torch.rsqrt(input=my_tensor) # tensor([[[1., inf], # [1., inf]], # [[inf, 1.], # [inf, 1.]]])
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