When moving along the second dimension (column by column), we need to move 1 step in the memory. The stride() return (3,1) means that: when moving along the first dimension by each step (row by row), we need to move 3 steps in the memory. First, let's create a contiguous tensor: aaa = torch.Tensor( ,] ) The contiguous() function is usually required when we first transpose() a tensor and then reshape (view) it. ntiguous() will create a copy of the tensor, and the element in the copy will be stored in the memory in a contiguous way. Some operations, such as reshape() and view(), will have a different impact on the contiguity of the underlying data. The tensor is not C contiguous anymore (it is in fact Fortran contiguous: each column is stored next to each other) > t.T.is_contiguous()Ĭontiguous() will rearrange the memory allocation so that the tensor is C contiguous: We need to skip 1 byte to go to the next line and 4 bytes to go to the next element in the same line. We need to skip 4 bytes to go to the next line, but only one byte to go to the next element in the same line.Īs said in other answers, some Pytorch operations do not change the memory allocation, only metadata. PyTorch's Tensor class method stride() gives the number of bytes to skip to get the next element in each dimension > t.stride() ![]() This is what PyTorch considers contiguous. ![]() The memory allocation is C contiguous if the rows are stored next to each other like this: Let's consider the 2D-array below: > t = torch.tensor(,, ]) It is not contiguous if the region of memory where it is stored looks like this:įor 2-dimensional arrays or more, items must also be next to each other, but the order follow different conventions. You're generally safe to assume everything will work, and wait until you get a RuntimeError: input is not contiguous where PyTorch expects a contiguous tensor to add a call to contiguous().Ī one-dimensional array is contiguous if its items are laid out in memory next to each other just like below: Normally you don't need to worry about this. When you call contiguous(), it actually makes a copy of the tensor such that the order of its elements in memory is the same as if it had been created from scratch with the same data. Here bytes are still allocated in one block of memory but the order of the elements is different! Note that the word "contiguous" is a bit misleading because it's not that the content of the tensor is spread out around disconnected blocks of memory. In the example above, x is contiguous but y is not because its memory layout is different to that of a tensor of same shape made from scratch. This is where the concept of contiguous comes in. In this example, the transposed tensor and original tensor share the same memory: x = torch.randn(3,2) Narrow(), view(), expand() and transpose()įor example: when you call transpose(), PyTorch doesn't generate a new tensor with a new layout, it just modifies meta information in the Tensor object so that the offset and stride describe the desired new shape. In this case, you can use torch.permute() to swap the last and the second dimension of the input tensor.There are a few operations on Tensors in PyTorch that do not change the contents of a tensor, but change the way the data is organized. However, your convolutional layer expects the input to have the shape (batch_size, channels, height, width). For instance, suppose you have an input tensor of shape (batch_size, height, width, channels), where channels are RGB values. One real-world scenario is when you want to change the order of the input channels for a convolutional layer. # ]]) Using torch.permute() in a neural network # Permute the tensor to have shape (4, 2, 3) # Create a random tensor of shape (2, 3, 4) This code changes the shape of a tensor from (2, 3, 4) to (4, 2, 3): import torch Each element of the tuple must be a valid index of a dimension of the input tensor, starting from zero.įor more clarity, see the examples below. The length of the tuple must match the number of dimensions of the input tensor. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |