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://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://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 contiguous in python. Before moving forward we should have a piece of knowledge about contiguous.

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

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

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），使得此时的offset和stride是与转置tensor相对应的。转置的tensor和原tensor的内存是共享的！举个栗子：运行结果： [ 1.2752, 1.9832], [ 1.9872, -1.2719]]) [ 0.8814, 1.9832, -1.2719]])

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

nn.Linear docs indicate it can take multi-dimensional inputs now and handle them. So the contiguous ().view (...) is not required now. Your example however may have code later on which works with the dimensions output with this model.

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://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://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

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