pytorch contiguous view - PrivacyWall

https://stackoverflow.com/questions/48915810/what-does-contiguous...

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What does .contiguous () do in PyTorch? - Stack Overflow

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)

https://zhuanlan.zhihu.com/p/545769141

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Pytorch系列：view()、permute()和contiguous()函数详解 - 知乎

3. 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 ...

https://docs.pytorch.org/docs/stable/generated/torch.Tensor.conti...

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torch.Tensor.contiguous — PyTorch 2.7 documentation

torch.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

https://medium.com/analytics-vidhya/pytorch-contiguous-vs-non-con...

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[Pytorch] Contiguous vs Non-Contiguous Tensor / View - Medium

Okay, 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.

https://discuss.pytorch.org/t/contigious-vs-non-contigious-tensor...

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Contigious vs non-contigious tensor - PyTorch Forums

Call .contiguous() before .view(). So let’s call .contiguous() before the view call: y = y.contiguous() print(y.stride()) > (4, 1) y = y.view(-1) Now the memory layout is contiguous once again (have a look at the strides) and the view works just fine. I’m not completely sure, but I assume the contiguous call copies the memory to make it ...

https://blog.csdn.net/qq_35608277/article/details/89667577

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pytorch .contiguous().view()_contiguous().view(n, -1, 4)-CSDN博客

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。

https://iifx.dev/en/articles/275277331

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Reshape vs. View in PyTorch: Memory Contiguity Explained

The 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 ...

https://stackoverflow.com/questions/42479902/what-does-view-do-in...

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python - What does `view ()` do in PyTorch? - Stack Overflow

Here you see PyTorch makes a tensor by converting an underlying block of contiguous memory into a matrix-like object by adding a shape and stride attribute:. shape states how long each dimension is; stride states how many steps you need to take in memory til you reach the next element in each dimension; view(dim1,dim2,...) returns a view of the same underlying information, but reshaped to a ...

https://discuss.pytorch.org/t/when-and-why-do-we-use-contiguous/4...

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When and why do we use Contiguous ()? - PyTorch Forums

Normally 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.

https://stackoverflow.com/questions/49643225/whats-the-difference...

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What's the difference between `reshape()` and `view()` in PyTorch?

torch.view has existed for a long time. It will return a tensor with the new shape. The returned tensor will share the underling data with the original tensor. See the documentation here.. On the other hand, it seems that torch.reshape has been introduced recently in version 0.4.According to the document, this method will. Returns a tensor with the same data and number of elements as input ...

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