718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
64 lines
2.1 KiB
Markdown
64 lines
2.1 KiB
Markdown
Mat structure is now allocator-aware via an extra allocator parameter with default zero value.
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The good-old ncnn::fastMalloc()/ncnn::fastFree() will be used for a null allocator.
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You could pass a custom allocator to delegate all memory allocation and deallocation.
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```cpp
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class Allocator
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{
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public:
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virtual void* fastMalloc(size_t size) = 0;
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virtual void fastFree(void* ptr) = 0;
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};
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```
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ncnn has already implemented two simple pooled Allocator class, with mutex lock or without it.
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```cpp
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ncnn::PoolAllocator locked_mempool;
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ncnn::UnlockedPoolAllocator unlocked_mempool;
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```
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the two allocator types in ncnn
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* blob allocator
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used to allocate memory for all named blobs, which you could retrieve by Extractor::extract()
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* workspace allocator
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used to allocate memory for internal temporary use in layer implementation, such as the temp blob after padding in convolution
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by default, all Extractor instance use the two allocator in the default option
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You can alter them by ncnn::set_default_option()
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or you can set them per Extractor by Extractor::set_blob_allocator()/Extractor::set_workspace_allocator()
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blob allocator is guaranteed to be called in-order in layer implementation during each Extractor lifecycle
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while workspace allocator may be called synchronously
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the practical usage
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* one network, one-by-one inference
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shared unlocked blob allocator for all Extractor
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shared locked workspace allocator for all Extractor
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* one network, concurrent inference
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shared unlocked blob allocator for all Extractor in each thread
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shared locked workspace allocator for all Extractor among all threads
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* concurrent multiple networks, one-by-one inference for each network
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shared unlocked blob allocator for all Extractor of each network
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shared locked workspace allocator for all Extractor among all networks (for saving memory)
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* concurrent multiple networks, concurrent inference for each network
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shared unlocked blob allocator for all Extractor of each network in each thread
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shared locked workspace allocator for all Extractor among all networks (for saving memory)
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