ComputeLibrary | Compute Library is a set of computer vision | Machine Learning library

 by   ARM-software C++ Version: v23.05 License: MIT

kandi X-RAY | ComputeLibrary Summary

kandi X-RAY | ComputeLibrary Summary

ComputeLibrary is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Raspberry Pi applications. ComputeLibrary has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Please report issues here: Documentation (API, changelogs, build guide, contribution guide, errata, etc.) available at Binaries available at
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              ComputeLibrary has a medium active ecosystem.
              It has 2479 star(s) with 748 fork(s). There are 234 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 15 open issues and 995 have been closed. On average issues are closed in 67 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of ComputeLibrary is v23.05

            kandi-Quality Quality

              ComputeLibrary has no bugs reported.

            kandi-Security Security

              ComputeLibrary has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              ComputeLibrary is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              ComputeLibrary releases are available to install and integrate.

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            ComputeLibrary Key Features

            No Key Features are available at this moment for ComputeLibrary.

            ComputeLibrary Examples and Code Snippets

            No Code Snippets are available at this moment for ComputeLibrary.

            Community Discussions

            QUESTION

            ComputeLibrary data type templates
            Asked 2018-Dec-29 at 10:52

            In the ARM ComputeLibrary, we can have a Tensor object of various types. When choosing the type of Tensors, we pass the type to the initaliser of the Tensor's allocator, such as float32 here:

            ...

            ANSWER

            Answered 2018-Dec-29 at 10:52

            Well... armcl types are, if I understand correctly, enum values.

            So a possible approach is a template struct, with full specializations, with a value in it.

            I mean... something as

            Source https://stackoverflow.com/questions/53968761

            QUESTION

            ComputeLibrary CLTensor data transfer
            Asked 2018-Dec-14 at 17:06

            I am working with integrating ARM ComputeLibrary into a project.

            It's not an API whose semantics I am familiar with, but I'm working my way through the docs and examples.

            At the moment, I am trying to copy the contents of an std::vector to a CLTensor. Then use the ARMCL GEMM operation.

            I've been building an MWE, shown below, with the aim of getting matrix multiplication working.

            To get the input data from a standard C++ std::vector, or std::ifstream, I am trying an iterator based approach, based on this example shown in the docs.

            However, I keep getting a segfault.

            There is an example of sgemm using CLTensor in the source, which is also where I'm drawing inspiration from. However it gets its input data from Numpy arrays, so isn't relevant up to this point.

            I'm not sure in ARMCL if CLTensor and Tensor have disjoint methods. But I feel like they are of a common interface ITensor. Still, I haven't been able to find an equivalent example that uses CLTensor instead of Tensor for this iterator based method.

            You can see my code I'm working with below, which fails on line 64 (*reinterpret_cast..). I'm not entirely sure what the operations are that it performs, but my guess is that we have our ARMCL iterator input_it which is incremented n * m times, each iteration setting the value of the CLTensor at that address to the corresponding input value. reinterpret_cast is just to make the types play nicely together?

            I reckon my Iterator and Window objects are okay, but can't be sure.

            ...

            ANSWER

            Answered 2018-Dec-14 at 17:06

            The part you've missed (Which admittedly could be better explained in the documentation!) is that you need to map / unmap OpenCL buffers in order to make them accessible to the CPU.

            If you look inside the fill_random_tensor (which is what's used in the cl_sgemm example you've got a call to tensor.map();

            So if you map() your buffer before creating your iterator then I believe it should work:

            Source https://stackoverflow.com/questions/53779230

            QUESTION

            Arm Compute Library - Canny Edge returns unusable data from imported opencv image
            Asked 2018-Oct-11 at 14:47

            I am working with the arm compute library link to convert an opencv application to a more efficient code base.

            I would like to import data from an opencv mat, which I've done successfully by doing this.

            ...

            ANSWER

            Answered 2018-Oct-11 at 14:47

            I found where I was going wrong and developed this function, which creates an OpenCV Mat from an ACL Image:

            Source https://stackoverflow.com/questions/52703756

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install ComputeLibrary

            You can download it from GitHub.

            Support

            Arm® CPUs: Arm® Cortex®-A processor family using Arm® Neon™ technology Arm® Cortex®-R processor family with Armv8-R AArch64 architecture using Arm® Neon™ technology Arm® Cortex®-X1 processor using Arm® Neon™ technologyArm® Mali™ GPUs: Arm® Mali™-G processor family Arm® Mali™-T processor familyx86
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