Concatenation is another important operation that you need in your toolbox. Two tensors of the same size on all the dimensions except one, if required, can be concatenated using cat. For example, a tensor of size 3 x 2 x 4 can be concatenated with another tensor of size 3 x 5 x 4 on the first dimension rapid lift fx to get a tensor of size 3 x 7 x 4. The stack operation looks very similar to concatenation but it is an entirely different operation. If you want to add a new dimension to your tensor, stack is the way to go. Similar to cat, you can pass the axis where you want to add the new dimension.

I would like to know if it is possible to realize a concatenation of contiguous and/or non-contiguous tensors without memory duplication. Today, the concatenation implemented on pytorch consists in the allocation of a new tensor. My guess is cache or memory rows are in the cat direction and not in the stack direction. This is how we understand the difference between the cat() and stack() functions. If you want to run a cat() function on the CPU, then we have to create a tensor with a cpu() function.

The main user-facing data structure in PyTorch is a THTensor object, which holds the information about dimension, offset, stride, and so on. However, another main piece of information THTensor stores is the pointer towards the THStorage object, which is an internal layer of the tensor object kept for storage. PyTorch has the anti-squeeze operation, called unsqueeze, which adds another fake dimension to your tensor object. Don’t confuse unsqueeze with stack, which also adds another dimension.

In this video, we want to concatenate PyTorch tensors along a given dimension. So, with this, we understood how the PyTorch 3D tensor are concatenate along 0 and -1 dimensions. In this section, we will learn how we can implement the PyTorch cat function with the help of an example in python. To concatenate two or more tensors by a row or column, use torch.cat(). In this simple example shown above, unsqueeze inserts a singleton dimension at the specified index 0.

For this, we pass the three tensors and dim parameter as ‘0’ to the stack() function and get a resulting tensor of 4X3 shape. We first import the PyTorch library and then with the tensor function, we create desired tensor sequences. These functions are analogous to numpy.stack and numpy.concatenate.

At that time, we can use Pytorch concatenate functionality as per requirement. Pytorch provides the torch.cat() function to concatenate the tensor. It uses different types of parameters such as tensor, dimension, and out. In this example, we will create five two-dimensional tensors and concatenate them via rows using torch.cat(). In this example, we will create five one-dimensional tensors and concatenate them row-wise using torch.cat(). This is a short example of the pytorch cat function, mostly for my own memory.