pytorch contiguous view - PrivacyWall

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

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

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. Normally you don't need to worry about this.

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

View uses the same data chunk from the original tensor, just a different way to ‘view’ its dimension. Before we dive into the discussion about what does contiguous vs. non-contiguous mean,...

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

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Reshape vs. View in PyTorch: Memory Contiguity Explained

view() operates on the existing data in memory. It can only change the shape of a tensor if the underlying memory layout is contiguous. If the tensor is not contiguous, view() will raise an error. If no copy is made, then just like view, the returned tensor shares the same memory as the original.

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

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

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。摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 > contiguous：view只能用在contiguous的variable上。如果在view之前用了transpose, permute等，需要用contiguous ()来返回一个contiguous copy。

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

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

如果想要断开这两个变量之间的依赖（x本身是contiguous的），就要使用contiguous ()针对x进行变化，感觉上就是我们认为的深拷贝。当调用contiguous ()时，会强制拷贝一份tensor，让它的布局和从头创建的一模一样，但是两个tensor完全没有联系。还是上面的栗子：运行结果： [ 0.0200, 0.5726], [-0.8865, -0.4727]])

https://www.geeksforgeeks.org/how-does-the-view-method-work-in-py...

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How Does the "View" Method Work in Python PyTorch?

Contiguous Tensors: The tensor you apply .view() to must be contiguous, meaning the data should be stored in a contiguous block of memory. If the tensor is not contiguous, you can make it contiguous by calling .contiguous() before .view(). tensor: The original tensor you want to reshape. shape: The desired shape of the output tensor.

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: 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 continuous again.

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

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torch.Tensor.view — PyTorch 2.7 documentation

When it is unclear whether a view() can be performed, it is advisable to use reshape(), which returns a view if the shapes are compatible, and copies (equivalent to calling contiguous()) otherwise. Returns a new tensor with the same data as the self tensor but of a different dtype.

https://pythonguides.com/pytorch-view/

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PyTorch View Tutorial [With 11 Examples] - Python Guides

In this section, we will learn about the PyTorch view in python. The PyTorch view () function is used to convert the tensor into a 2D format which is rows and columns. And we should have a definite number of rows and columns to view. Syntax: The syntax of the PyTorch view is : Parameters: no_of_rows: There are several rows that we can view.

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