This would avoid the temporary buffers from the current implementation, and would probably be faster as well. Looking at the code, there might be a more memory efficient Python implementation as well, save an MxNx2 allocation by inplace operations. This would increase the threshold but still have its limits.
Sorry, I was slow on the kernel uptake. I thought you meant casting it as a constitutional kernel, which seemed odd. A more memory efficient kernel would be great for my current use case, e. There are other workarounds cropping so it likely shouldn't be the highest item on your list.
I'm using x images, but junk yards and parking lots can pack in a lot of GT targets. I think we can use some in-place operations there, and it will bring some savings, but I'm not sure by how much. Chunking is a reasonable compromise as well I think.
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IOU matrix is very often extremely sparse, especially if you immediately remove bbox matches with IOU less than predefined threshold which might be 0. Is it a good idea to add iou computation result to be a sparse matrix or at least add it as an option? There is currently limited support for sparse matrices in PyTorch, so it might not be ideal for now. We currently need max over a dimension for it to work, and I this function is currently not supported yet. But that might be non-trivial to do. I don't think it's a good choice to do like this.
I calculate iou in four ways: numpy version, torch version, cython version and gpu version. Indeed, gpu version is fastest. But it costs a lot of memory. Numpy version is close to the torch version but is much slower than cython version.
So I suggest using the cython version can refer to detectron. You can also use the torch. Try something like this instead. Note that you'll need to unwrap the boxlist in a separate function, to pass the Tensors directly to this function. Why is it not used in the master code when it seems like a very good improvement?
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2degrees IOU & Pack Assist
It's not in master because it makes things slightly less unreadable. Skip to content. Dismiss Join GitHub today GitHub is home to over 36 million developers working together to host and review code, manage projects, and build software together. Sign up. New issue.
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Any suggestions? This comment has been minimized. Sign in to view. I'll think about implementing it. Feel free to close the issue and maybe open it as an enhancement? I've changed the title of the issue, let's keep it open. The box order must be xmin, ymin, xmax, ymax. Arguments: box1: BoxList bounding boxes, sized [N,4]. Returns: tensor iou, sized [N,M]. Here fix the number of gt boxes, and use cpu mode to compute IoU, then convert to gpu mode w.
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But I'm not familiar with torch. Our in-depth tools give millions of people across the globe highly detailed and thoroughly explained answers to their most important financial questions. Each month, more than 1 million visitors in countries across the globe turn to InvestingAnswers.
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