基于 C++和python实现BlazePose算法的机器人人体姿势识别与模仿算法源码.zip

<hr> <h3>About CUB</h3> CUB provides state-of-the-art, reusable software components for every layer of the CUDA programming model: - [<b><em>Device-wide primitives</em></b>](https://nvlabs.github.com/cub/group___device_module.html) - Sort, prefix scan, reduction, histogram, etc. - Compatible with CUDA dynamic parallelism - [<b><em>Block-wide "collective" primitives</em></b>](https://nvlabs.github.com/cub/group___block_module.html) - I/O, sort, prefix scan, reduction, histogram, etc. - Compatible with arbitrary thread block sizes and types - [<b><em>Warp-wide "collective" primitives</em></b>](https://nvlabs.github.com/cub/group___warp_module.html) - Warp-wide prefix scan, reduction, etc. - Safe and architecture-specific - [<b><em>Thread and resource utilities</em></b>](https://nvlabs.github.com/cub/group___thread_module.html) - PTX intrinsics, device reflection, texture-caching iterators, caching memory allocators, etc.  CUB is included in the NVIDIA HPC SDK and the CUDA Toolkit. We recommend the [CUB Project Website](http://nvlabs.github.com/cub) for further information and examples. <br><hr> <h3>A Simple Example</h3> ```C++ #include <cub/cub.cuh> // Block-sorting CUDA kernel __global__ void BlockSortKernel(int *d_in, int *d_out) { using namespace cub; // Specialize BlockRadixSort, BlockLoad, and BlockStore for 128 threads // owning 16 integer items each typedef BlockRadixSort<int, 128, 16> BlockRadixSort; typedef BlockLoad<int, 128, 16, BLOCK_LOAD_TRANSPOSE> BlockLoad; typedef BlockStore<int, 128, 16, BLOCK_STORE_TRANSPOSE> BlockStore; // Allocate shared memory __shared__ union { typename BlockRadixSort::TempStorage sort; typename BlockLoad::TempStorage load; typename BlockStore::TempStorage store; } temp_storage; int block_offset = blockIdx.x * (128 * 16); // OffsetT for this block's ment // Obtain a segment of 2048 consecutive keys that are blocked across threads int thread_keys[16]; BlockLoad(temp_storage.load).Load(d_in + block_offset, thread_keys); __syncthreads(); // Collectively sort the keys BlockRadixSort(temp_storage.sort).Sort(thread_keys); __syncthreads(); // Store the sorted segment BlockStore(temp_storage.store).Store(d_out + block_offset, thread_keys); } ``` Each thread block uses `cub::BlockRadixSort` to collectively sort its own input segment. The class is specialized by the data type being sorted, by the number of threads per block, by the number of keys per thread, and implicitly by the targeted compilation architecture. The `cub::BlockLoad` and `cub::BlockStore` classes are similarly specialized. Furthermore, to provide coalesced accesses to device memory, these primitives are configured to access memory using a striped access pattern (where consecutive threads simultaneously access consecutive items) and then <em>transpose</em> the keys into a [<em>blocked arrangement</em>](index.html#sec4sec3) of elements across threads. Once specialized, these classes expose opaque `TempStorage` member types. The thread block uses these storage types to statically allocate the union of shared memory needed by the thread block. (Alternatively these storage types could be aliased to global memory allocations). <br><hr> <h3>Releases</h3> CUB is distributed with the NVIDIA HPC SDK and the CUDA Toolkit in addition to GitHub. See the [changelog](CHANGELOG.md) for details about specific releases. | CUB Release | Included In | | ------------------------- | ------------------------------ | | 1.9.10 | NVIDIA HPC SDK 20.5 | | 1.9.9 | CUDA Toolkit 11.0 | | 1.9.8-1 | NVIDIA HPC SDK 20.3 | | 1.9.8 | CUDA Toolkit 11.0 Early Access | | 1.9.8 | CUDA 11.0 Early Access | | 1.8.0 | | | 1.7.5 | Thrust 1.9.2 | | 1.7.4 | Thrust 1.9.1-2 | | 1.7.3 | | | 1.7.2 | | | 1.7.1 | | | 1.7.0 | Thrust 1.9.0-5 | | 1.6.4 | | | 1.6.3 | | | 1.6.2 (previously 1.5.5) | | | 1.6.1 (previously 1.5.4) | | | 1.6.0 (previously 1.5.3) | | | 1.5.2 | | | 1.5.1 | | | 1.5.0 | | | 1.4.1 | | | 1.4.0 | | | 1.3.2 | | | 1.3.1 | | | 1.3.0 | | | 1.2.3 | | | 1.2.2 | | | 1.2.0 | | | 1.1.1 | | | 1.0.2 | | | 1.0.1 | | | 0.9.4 | | | 0.9.2 | | | 0.9.1 | | | 0.9.0 | | <br><hr> <h3>Development Model</h3> For information on development model, see [this document](DEVELOPMENT_MODEL.md). <br><hr> <h3>Open Source License</h3> CUB is available under the "New BSD" open-source license: ``` Copyright (c) 2010-2011, Duane Merrill. All rights reserved. Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ```