srch

dtype: Type of data. Note that type of all the elements of a tensor must be the same. device: To tell if GPU or CPU should be used.

float32 . This data type will use 4kb for each value in the tensor (check using .element_size() ), which will give a total of ~48GB ...

The unique function returns only one 0 from the zero matrix, as stated before, and the function returns an error because the test tensor is no ...

torch. all (input) → Tensor. Tests if all elements in input evaluate to True . Note. This function matches the behaviour of NumPy in returning output of ...

2 Answers · To know whether a tensor is allocated (type and storage), use defined(). · To know whether an allocated tensor has zero elements, use ...

torch.count_nonzero. torch. count_nonzero (input, dim=None) → Tensor. Counts the number of non-zero values in the tensor input along the given dim . If no ...

torch.is_nonzero. torch. is_nonzero (input). Returns True if the input is a single element tensor which is not equal to zero after type conversions.

06.11.2018 · A leaf tensor is a tensor with no parent in the graph. If it does not require grad, then it never has parents and is always a leaf, if it requires grad, it is a leaf only if it was created by the user (not the result of an op). When running the backward pass, the gradients are accumulated in all the leaf tensors that require gradients.

Apr 18, 2018 · I recently profiled my model code and one surprising thing that came up was how slow checking if a tensor is all 0’s is. More specifically, my_tensor.sum().data[0] == 0 takes upwards of 0.01 seconds per execution which can add up. I’m guessing this is because ‘my_tensor’ is a variable on GPU and must be converted to CPU before doing the boolean check. My question is whether there is a ...

Mar 08, 2018 · just beware that you can have a tensor with no elements and non-trivial size. t = torch.zeros(size=(1,0)) t.nelement() # returns zero, empty in this sense len(t.size()) # returns two, not empty in this sense!! len(t) # returns one, not empty in this sense!!

This solves OP's problem but does not actually check if the row is all zero, but that the sum is zero. A row might contain [100, -100,0,0,0] and this will sum to zero. If you switched a few things around, then it could be made to work consistently in other scenarios and match title of OP's question more accurately.

Learn about PyTorch’s features and capabilities. Community. Join the PyTorch developer community to contribute, learn, and get your questions answered. Developer Resources. Find resources and get questions answered. Forums. A place to discuss PyTorch code, issues, install, research. Models (Beta) Discover, publish, and reuse pre-trained models

18.04.2018 · I recently profiled my model code and one surprising thing that came up was how slow checking if a tensor is all 0’s is. More specifically, my_tensor.sum().data[0] == 0 takes upwards of 0.01 seconds per execution which can add up. I’m guessing this is because ‘my_tensor’ is a variable on GPU and must be converted to CPU before doing the boolean …

Returns True if obj is a PyTorch tensor. Note that this function is simply doing isinstance(obj, Tensor) . Using that isinstance check is better for ...

torch.zeros¶ torch. zeros (*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor ¶ Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument size.. Parameters. size (int...) – a sequence of integers defining the shape of the output tensor.

I'm guessing this is because 'my_tensor' is a variable on GPU and must be converted to CPU before doing the boolean check. My question is whether there is a ...

17.07.2020 · Hello @pascal.soveaux. I am actually very interested to understand why my solution is wrong. Briefly, my goal is to move the values of test_sensor to test_tensor_2 and then turn test_tensor to a zero tensor without allocating new memory anywhere in the process (if possible). I can sacrifice a bit of computation time for this but not memory.

19.12.2020 · I have a tensor t = torch.zeros((4, 5, 6)) How to check if it is on gpu or not?

Jul 01, 2017 · You can only call torch.nonzero() on a simple tensor, not a variable. It makes sens: I doubt that the counting of non-zero element would be differentiable. but you have sum( abs( x/ (abs(x) + epsilon) )), that approximates the number of zero, and is differentiable.

torch.is_tensor¶ torch. is_tensor (obj) [source] ¶ Returns True if obj is a PyTorch tensor.. Note that this function is simply doing isinstance(obj, Tensor).Using that isinstance check is better for typechecking with mypy, and more explicit - so it’s recommended to use that instead of is_tensor.

torch.is_tensor¶ torch. is_tensor (obj) [source] ¶ Returns True if obj is a PyTorch tensor.. Note that this function is simply doing isinstance(obj, Tensor).Using that isinstance check is better for typechecking with mypy, and more explicit - so it’s recommended to use that instead of is_tensor.. Parameters. obj (Object) – Object to test. Example: >>> x = torch. tensor ([1, 2, 3 ...

22.08.2019 · Is there a way in Pytorch to check if something is a number(dim()==0) or a tensor(dim() >0) Shisho_Sama (A curious guy here!) August 22, 2019, 5:23am

If input has n n n dimensions, then the resulting tuple contains n n n tensors of size z z z, where z z z is the total number of non-zero elements in the input tensor. As a special case, when input has zero dimensions and a nonzero scalar value, it is treated as a one-dimensional tensor with one element. Parameters. input – the input tensor.

torch.count_nonzero(input, dim=None) → Tensor. Counts the number of non-zero values in the tensor input along the given dim . If no dim is specified then all non-zeros in the tensor are counted. Parameters. input ( Tensor) – the input tensor. dim ( int or tuple of python:ints, optional) – Dim or tuple of dims along which to count non-zeros.

I want to judge whether a tensor is an empty tensor. ... t = torch.zeros(size=(1,0)) t.nelement() # returns zero, empty in this sense len(t.size()) ...

If input has n n n dimensions, then the resulting tuple contains n n n tensors of size z z z, where z z z is the total number of non-zero elements in the input tensor. As a special case, when input has zero dimensions and a nonzero scalar value, it is treated as a one-dimensional tensor with one element. Parameters. input – the input tensor.