What is the difference between torch.tensor and torch.Tensor?

According to discussion on pytorch discussion  torch.Tensor constructor is overloaded to do the same thing as both torch.tensor and torch.empty. It is thought this overload would make code confusing, so split torch.Tensor into torch.tensor and torch.empty.

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

In PyTorch torch.Tensor is the main tensor class. So all tensors are just instances of torch.Tensor.

When you call torch.Tensor() you will get an empty tensor without any data.

In contrast torch.tensor is a function which returns a tensor. In the documentation it says:

torch.tensor(data, dtype=None, device=None, requires_grad=False) → Tensor

Constructs a tensor with data.

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This also explains why it is no problem creating an empty tensor instance of `torch.Tensor` without `data` by calling:

tensor_without_data = torch.Tensor()

But on the other side:

tensor_without_data = torch.tensor()

Will lead to an error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-12-ebc3ceaa76d2> in <module>()
----> 1 torch.tensor()

TypeError: tensor() missing 1 required positional arguments: "data"

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But in general there is no reason to choose `torch.Tensor` over `torch.tensor`. Also `torch.Tensor` lacks a docstring.

Similar behaviour for creating a tensor without data like with: torch.Tensor() can be achieved using:

torch.tensor(())

Output:

tensor([])

In addition to the above answers, I noticed:

• torch.Tensor() creates a tensor with the default data type, as defined by torch.get_default_dtype().
• torch.tensor() will infer data type from the data.

For example:

>>> torch.Tensor([1, 2, 3]).dtype
torch.float32

>>> torch.tensor([1, 2, 3]).dtype
torch.int64

https://discuss.pytorch.org/t/difference-between-torch-tensor-and-torch-tensor/30786/2

torch.tensor infers the dtype automatically, while torch.Tensor returns a torch.FloatTensor. I would recommend to stick to torch.tensor, which also has arguments like dtype, if you would like to change the type.