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* Doc Migration from Gitlab (#1289) * doc migration * fix * Update FakeQuantize_1.md * Update performance_benchmarks.md * Updates graphs for FPGA * Update performance_benchmarks.md * Change DL Workbench structure (#1) * Changed DL Workbench structure * Fixed tags * fixes * Update ie_docs.xml * Update performance_benchmarks_faq.md * Fixes in DL Workbench layout * Fixes for CVS-31290 * [DL Workbench] Minor correction * Fix for CVS-30955 * Added nGraph deprecation notice as requested by Zoe * fix broken links in api doxy layouts * CVS-31131 fixes * Additional fixes * Fixed POT TOC * Update PAC_Configure.md PAC DCP 1.2.1 install guide. * Update inference_engine_intro.md * fix broken link * Update opset.md * fix * added opset4 to layout * added new opsets to layout, set labels for them * Update VisionAcceleratorFPGA_Configure.md Updated from 2020.3 to 2020.4 Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
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Inference Engine Memory primitives
Blobs
InferenceEngine::Blob is the main class intended for working with memory.
Using this class you can read and write memory, get information about the memory structure etc.
The right way to create Blob objects with a specific layout is to use constructors with InferenceEngine::TensorDesc.
InferenceEngige::TensorDesc tdesc(FP32, {1, 3, 227, 227}, InferenceEngine::Layout::NCHW);
InferenceEngine::Blob::Ptr blob = InferenceEngine::make_shared_blob(tdesc);
Layouts
InferenceEngine::TensorDesc is a special class that provides layout format description.
This class allows to create planar layouts using the standard formats (like InferenceEngine::Layout::NCDHW, InferenceEngine::Layout::NCHW, InferenceEngine::Layout::NC, InferenceEngine::Layout::C and etc) and also non-planar layouts using InferenceEngine::BlockingDesc.
In order to create a complex layout you should use InferenceEngine::BlockingDesc which allows to define the blocked memory with offsets and strides.
Examples
- You can define a blob with dimensions {N: 1, C: 25, H: 20, W: 20} and format NHWC with using next parameters:
InferenceEngine::BlockingDesc({1, 20, 20, 25}, {0, 2, 3, 1}); // or
InferenceEngine::BlockingDesc({1, 20, 20, 25}, InferenceEngine::Layout::NHWC);
- If you have a memory with real dimensions {N: 1, C: 25, H: 20, W: 20} but with channels which are blocked by 8, you can define it using next parameters:
InferenceEngine::BlockingDesc({1, 4, 20, 20, 8}, {0, 1, 2, 3, 1})
- Also you can set strides and offsets if layout contains it.
- If you have a complex blob layout and you don't want to calculate the real offset to data you can use methods
InferenceEngine::TensorDesc::offset(size_t l)orInferenceEngine::TensorDesc::offset(SizeVector v).
For example:
InferenceEngine::BlockingDesc blk({1, 4, 20, 20, 8}, {0, 1, 2, 3, 1});
InferenceEngine::TensorDesc tdesc(FP32, {1, 25, 20, 20}, blk);
tdesc.offset(0); // = 0
tdesc.offset(1); // = 8
tdesc.offset({0, 0, 0, 2}); // = 16
tdesc.offset({0, 1, 0, 2}); // = 17
- If you would like to create a TensorDesc with a planar format and for N dimensions (N can be different 1, 2, 4 and etc), you can use the method
InferenceEngine::TensorDesc::getLayoutByDims.
InferenceEngine::TensorDesc::getLayoutByDims({1}); // InferenceEngine::Layout::C
InferenceEngine::TensorDesc::getLayoutByDims({1, 2}); // InferenceEngine::Layout::NC
InferenceEngine::TensorDesc::getLayoutByDims({1, 2, 3, 4}); // InferenceEngine::Layout::NCHW
InferenceEngine::TensorDesc::getLayoutByDims({1, 2, 3}); // InferenceEngine::Layout::BLOCKED
InferenceEngine::TensorDesc::getLayoutByDims({1, 2, 3, 4, 5}); // InferenceEngine::Layout::NCDHW
InferenceEngine::TensorDesc::getLayoutByDims({1, 2, 3, 4, 5, ...}); // InferenceEngine::Layout::BLOCKED