What is the difference between Tensor and tensor? Is Tensor going to be deprecated in the future?

1

I am using pytorch 0.4.

I get the following error when I use capitalized Tensor

>>> torch.Tensor(1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3.6/site-packages/torch/tensor.py", line 57, in __repr__
    return torch._tensor_str._str(self)
  File "/usr/lib/python3.6/site-packages/torch/_tensor_str.py", line 218, in _str
    fmt, scale, sz = _number_format(self)
  File "/usr/lib/python3.6/site-packages/torch/_tensor_str.py", line 96, in _number_format
    if value != math.ceil(value.item()):
RuntimeError: Overflow when unpacking long

but no problem if I use torch.tensor(1).

However, one good thing about capitalized Tensor is that I can specify shape like torch.Tensor(2,3) which gives me

tensor([[-1.0365e+18,  3.0754e-41,  5.5266e+30],
        [ 3.0754e-41,  4.4842e-44,  0.0000e+00]])

I can’t do that with torch.tensor(2,3) which gives me

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: tensor() takes 1 positional argument but 2 were given

Are all these behaviors expected? When should I use which?

5 Likes
2

There’s a bug in printing tensors where printing a tensor with a very large value causes a Runtime error.

You should use torch.empty in place of torch.Tensor.

3

torch.tensor is similar with numpy.array and also used to create scalar.

a = torch.tensor([1,2]) # accept list
a = torch.tensor(1) # scalar
a = torch.Tensor(2,3) # Tensor
1 Like
4

Isn’t torch.tensor also Tensor?

5

@richard So is it faster and more memory efficient to use torch.empty() instead of torch.Tensor(), when defining empty tensors in a class initialization function?

6

Our torch.Tensor constructor is overloaded to do the same thing as both torch.tensor and torch.empty. We thought this overload would make code confusing, so we split torch.Tensor into torch.tensor and `torch.empty.

So @yxchng yes, to some extent, torch.tensor works similarly to torch.Tensor (when you pass in data). @ProGamerGov no, neither should be more efficient than the other. It’s just that the torch.empty and torch.tensor have a nicer API than our legacy torch.Tensor constructor.

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7

torch.Tensor is a kind of mixture between torch.empty and torch.tensor , but when pass in data, torch.Tensor uses a global default dtype, torch.tensor infers data type from data

3 Likes
8

It looks like the torch.tensor is better than torch.Tensor when realize the same function?

I found a case to use Tensor rather than tensor:

torch.Tensor with int / float

In[] : 1/torch.Tensor([1,2,3])
Out[]: tensor([ 1.0000,  0.5000,  0.3333])

torch.tensor with int

In[] : 1/torch.tensor([1,2,3])
------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-231-55a86778a1f4> in <module>()
----> 1 1/torch.tensor([1,2,3]).
.../anaconda3/lib/python3.6/site-packages/torch/tensor.py in __rdiv__(self, other)
    318 
    319     def __rdiv__(self, other):
--> 320         return self.reciprocal() * other
    321     __rtruediv__ = __rdiv__
    322     __itruediv__ = _C._TensorBase.__idiv__

RuntimeError: reciprocal is not implemented for type torch.LongTensor

torch.tensor with float

In []: 1/torch.tensor([1.,2.,3.])
Out[]: tensor([ 1.0000,  0.5000,  0.3333])
1 Like
9

One important thing is that torch.tensor(1) gives you a fixed value of 1, while torch.Tensor(1) gives you a tensor of size 1 which is randomly initialized

2 Likes
10

So when do we use which?

1 Like
11

Well it depends on your use case, as my reply mentioned. If you want to explicitly specify what the tensor is, then use lowercase tensor

12

Update - Now it considers as integer.

import torch

a = torch.tensor([1, 2, 3])

print(1/a)

Here the output is [1, 0, 0]. Technically // operation is done.

Thanks

13

answer to the title of the question: