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From the pytorch forum. use t.is_cuda t = torch.randn(2,2) t.is_cuda # returns False t = torch.randn(2,2).cuda() t.is_cuda # returns True.

torch.is_nonzero(input) Returns True if the input is a single element tensor which is not equal to zero after type conversions. i.e. not equal to torch.tensor ( [0.]) or torch.tensor ( [0]) or torch.tensor ( [False]) . Throws a RuntimeError if torch.numel () != 1 (even in case of sparse tensors).

If input has n n n dimensions, then the resulting indices tensor out is of size (z × n) (z \times n) (z × n), where z z z is the total number of non-zero elements in the input tensor. When as_tuple is True: Returns a tuple of 1-D tensors, one for each dimension in input, each containing the indices (in that dimension) of all non-zero elements of input.

torch.all(input, dim, keepdim=False, *, out=None) → Tensor. For each row of input in the given dimension dim , returns True if all elements in the row evaluate to True and False otherwise. If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1.

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. requires_grad ...

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.

In this article, I'm going to go over some useful tensor functions I discovered in PyTorch, a Python library for deep learning. To build a brain on a ...

Let's get started with what is PyTorch, It is a deep learning framework that ... If the dimensions of the input tensor and batches multiplication do not ...

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.

01.09.2019 · Hello everyone! I have a tensor, let’s say A, which contains zero rows. I mean rows with all zero elements. There are other rows with non-zero elements as well. I there any elegant way to remove all zero’ed rows? To she…

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 ...

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

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 …

An approach to find if a row of a tensor is all zero: import tensorflow as tf image = tf.fill([8,8], 0) sess = tf.Session() sess.run(tf.initialize_all_variables()) image_row = tf.slice(image, [1,0], [1, -1]) total = tf.reduce_sum(tf.abs(image_row)) is_all_zero = tf.equal(total, 0) print sess.run([total, is_all_zero, image_row])

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.

torch. all (input, dim, keepdim = False, *, out = None) → Tensor. For each row of input in the given dimension dim, returns True if all elements in the row evaluate to True and False otherwise. If keepdim is True, the output tensor is of the same size as input except in …

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 ...

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

Apr 18, 2018 · Checking tensor is all 0's tensor.sum().data[0] == 0 extremely slow - PyTorch Forums. 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 whic… 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.

01.07.2017 · I want to find the number of non-zero elements in a tensor along a particular axis. Is there any PyTorch function which can do this? I tried to use the nonzero() method in PyTorch. torch.nonzero(losses).size(0) Here…

Returns a tuple of 1-D tensors, one for each dimension in input, each containing the indices (in that dimension) of all non-zero elements of input. 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.

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.