How to Fold a Tensor After Unfolding with Overlap in PyTorch скачать в хорошем качестве

How to Fold a Tensor After Unfolding with Overlap in PyTorch

Learn how to efficiently fold a tensor that has been unfolded with overlap in PyTorch, ensuring you restore original data correctly. --- This video is based on the question https://stackoverflow.com/q/65327458/ asked by the user 'Shamoon' ( https://stackoverflow.com/u/239879/ ) and on the answer https://stackoverflow.com/a/65328184/ provided by the user 'Proko' ( https://stackoverflow.com/u/9730862/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How can I fold a Tensor that I unfolded with PyTorch that has overlap? Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Understanding Tensor Folding in PyTorch When working with tensors in PyTorch, especially in the context of deep learning and data processing, it's common to manipulate their shapes for various tasks. One such operation is folding a tensor that has been previously unfolded with overlaps. This can be quite tricky if you're not familiar with the underlying mechanics. In this post, we'll walk through how to fold a tensor back to its original shape after it has been unfolded, ensuring you correctly handle any overlapping data. The Problem Statement Let's say you have a tensor with a size of torch.Size([1, 63840]). You decide to manipulate this tensor, specifically by unfolding it like so: [[See Video to Reveal this Text or Code Snippet]] After this operation, you end up with a tensor of the shape torch.Size([1, 797, 160]). At this point, you want to know how to fold this tensor back to its original size of torch.Size([1, 63840]) while preserving its integrity. Key Conditions for Unfolding and Folding Before we dive into the solution, it's crucial to understand two important conditions that apply to the unfold operation: Length of the Unfold: The first parameter n must be greater than or equal to the step size s. If n < s, you will skip some original data, and therefore you cannot restore the original tensor accurately. Data Limitations: The total length of the unfolded tensor (n + s) must be less than or equal to the dimension of the tensor you started with, i.e., t.shape[i]. The Solution: How to Fold the Tensor To fold the tensor back to its original shape, you can use the following custom function: [[See Video to Reveal this Text or Code Snippet]] Breakdown of the Function x[0]: This is the first piece of your unfolded tensor, which is unique and should always be kept as is. x[1:][:, n-s:]: This part captures the remaining sections of your tensor but ignores the overlapping parts. Here, n-s determines how many elements will overlap between the unfolded segments, and thus you only want to include fresh data post these overlaps. Example Illustration To illustrate how this folding process maintains the integrity of the data, consider the following example: [[See Video to Reveal this Text or Code Snippet]] This results in a tensor like: [[See Video to Reveal this Text or Code Snippet]] In this setup, the repeated elements signal where overlap occurs. Using the Function You can then test the folding function as follows: [[See Video to Reveal this Text or Code Snippet]] The output will return to the original tensor, demonstrating the efficiency of our fold operation. Conclusion Folding tensors after an unfolding operation with overlap in PyTorch is manageable as long as you adhere to the key conditions and utilize the outlined roll function effectively. This approach ensures that you efficiently reconstruct your original tensor without losing any important data due to overlaps. With this newfound knowledge, you can better manipulate and reshape tensors in your projects, enhancing your workflows in deep learning and data processing tasks.