New! PrivateView
Beta
The memory allocation is C contiguous if the rows are stored next to each other like this: This is what PyTorch considers contiguous. >>> t.is_contiguous() True PyTorch's Tensor class method stride() gives the number of bytes to skip to get the next element in each dimension >>> t.stride() (4, 1)
Copied 
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
BetaOkay, now we finished the introduction of contiguous view, and also learned how the strides works in a N dimensional tensor in Pytorch. Now let’s take a look at what the non-contiguous data is like.
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
Betatorch.Tensor.contiguous¶ Tensor. contiguous (memory_format = torch.contiguous_format) → Tensor ¶ Returns a contiguous in memory tensor containing the same data as self tensor. If self tensor is already in the specified memory format, this function returns the self tensor. Parameters
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
BetaThe key difference revolves around how PyTorch handles the underlying memory of the tensor.Non-Contiguous Memory If a tensor has been manipulated (e.g., by transposing or slicing), its data might be scattered in memory ... Contiguity Always be mindful of contiguity when using view(). Use contiguous() if needed. python pytorch ...
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
Beta文章浏览阅读8.5k次，点赞7次，收藏23次。本文详细解析了PyTorch中view函数的使用方法及其与contiguous函数的关系。解释了为何某些情况下需要使用contiguous确保tensor在内存中连续分布，以便正确执行view操作。同时介绍了view函数中-1参数的含义，以及如何利用它进行灵活的张量重塑。
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
Beta3. contiguous() 在 pytorch 中只有很少几个操作是不改变tensor内容本身的，大多数操作不进行数据拷贝和数据的改变，变的是原数据。 例如：narrow()、view()、expand()和 transpose() 等。. 例如使用transpose()进行转置操作时，pytorch并不会创建新的、转置后的 tensor ，而是修改了tensor中的一些属性（也就是原数据 ...
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
BetaNormally some changes like view(..), transpose(...) or permute(..) would just change the metadata (being lazy) and not the underlying storage. This create issues with parallel computations. Inorder to consolidate it into a contiguous memory as expected by other ops, contiguous() is called.
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
Betaview (dtype) → Tensor. Returns a new tensor with the same data as the self tensor but of a different dtype.. If the element size of dtype is different than that of self.dtype, then the size of the last dimension of the output will be scaled proportionally.For instance, if dtype element size is twice that of self.dtype, then each pair of elements in the last dimension of self will be combined ...
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
BetaOutput: torch.Size([6, 4]) In most cases, .reshape() is more flexible and safer to use, but .view() is more efficient when working with contiguous tensors. Conclusion. The .view() function in PyTorch is a powerful tool for reshaping tensors efficiently. It allows you to alter the shape of a tensor without changing its data, provided that the tensor is contiguous.
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
New! PrivateView
BetaIn PyTorch, a tensor's memory layout is considered contiguous if its elements are stored in memory in the same order as they appear when you iterate over the tensor using its shape. ... z = y.contiguous().view(12) y.contiguous(): This creates a new tensor that has the same data as y but with a contiguous memory layout. Now, the elements are ...
Copied
See Cached Version
Your search and this result
- The search term appears in the result: pytorch contiguous view
- The website matches one or more of your search terms
- Other websites that include your search terms link to this result
- The result is in Malti
