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MO dev guide refactoring (#3266) (#3595)

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* Release mo dev guide refactoring (#3266)

* Updated MO extension guide

* Minor change and adding svg images

* Added additional information about operation extractors. Fixed links and markdown issues

* Added missing file with information about Caffe Python layers and image for MO transformations dependencies graph

* Added section with common graph transformations attributes and diagram with anchor transformations. Added list of available front phase transformations

* Added description of front-phase transformations except the scope-defined and points defined. Removed legacy document and examples for such transformations.

* Added sections about node name pattern defined front phase transformations. Copy-pasted the old one for the points defined front transformation

* Added description of the rest of front transformations and and all middle and back phase transformations

* Refactored Legacy_Mode_for_Caffe_Custom_Layers and updated the Customize_Model_Optimizer with information about extractors order

* Added TOC for the MO Dev guide document and updated SVG images with PNG ones

* Fixed broken link. Removed redundant image

* Fixed broken links

* Added information about attributes 'run_not_recursively', 'force_clean_up' and 'force_shape_inference' of the transformation

* Code review comments

* Added a section about `Port`s

* Extended Ports description with examples

* Added information about Connections

* Updated MO README.md and removed a lot of redundant and misleading information

* Updates to the Customize_Model_Optimizer.md

* More updates to the Customize_Model_Optimizer.md

* Final updates for the Customize_Model_Optimizer.md

* Fixed some broken links

* More fixed links

* Refactored Custom Layers Guide: removed legacy and incorrect text, added up-to-date.

* Draft implementation of the Custom layer guide example for the MO part

* Fixed broken links using #. Change layer->operation in extensibility documents

* Updated Custom operation guide with IE part

* Fixed broken links and minor updates to the Custom Operations Guide

* Updating links

* Layer->Operation

* Moved FFTOp implementation to the template extension

* Update the CMake for template_extension to build the FFT op conditionally

* Fixed template extension compilation

* Fixed CMake for template extension

* Fixed broken snippet

* Added mri_demo script and updated documentation

* One more compilation error fix

* Added missing header for a demo file

* Added reference to OpenCV

* Fixed unit test for the template extension

* Fixed typos in the template extension

* Fixed compilation of template extension for case when ONNX importer is disabled

Co-authored-by: Alexander Zhogov <alexander.zhogov@intel.com>
This commit is contained in:
Evgeny Lazarev
2021-01-14 16:28:53 +03:00
committed by GitHub
parent 08afa4fd97
commit dbad8809bf
46 changed files with 2391 additions and 2081 deletions
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119
docs/template_extension/fft_kernel.cpp Normal file
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// Copyright (C) 2020 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
//! [fft_kernel:implementation]
#include "fft_kernel.hpp"
#include "fft_op.hpp"
#include <details/ie_exception.hpp>
#include <ie_layouts.h>
#include <opencv2/opencv.hpp>
using namespace TemplateExtension;
FFTImpl::FFTImpl(const std::shared_ptr<ngraph::Node> &node) {
auto castedNode = std::dynamic_pointer_cast<FFTOp>(node);
if (!castedNode)
THROW_IE_EXCEPTION << "Cannot create implementation for unknown operation!";
if (castedNode->inputs().size() != 1 || castedNode->outputs().size() != 1)
THROW_IE_EXCEPTION << "Cannot create implementation for operation with incorrect number of inputs or outputs!";
if (castedNode->get_input_partial_shape(0).is_dynamic() || castedNode->get_output_partial_shape(0).is_dynamic())
THROW_IE_EXCEPTION << "Cannot create implementation for op with dynamic shapes!";
if (castedNode->get_input_element_type(0) != ngraph::element::f32 || castedNode->get_output_element_type(0) != ngraph::element::f32)
THROW_IE_EXCEPTION << "Operation supports only FP32 tensors.";
inpShape = castedNode->get_input_shape(0);
outShape = castedNode->get_output_shape(0);
inverse = castedNode->inverse;
}
InferenceEngine::StatusCode FFTImpl::getSupportedConfigurations(std::vector<InferenceEngine::LayerConfig> &conf,
InferenceEngine::ResponseDesc *resp) noexcept {
std::vector<InferenceEngine::DataConfig> inDataConfig;
std::vector<InferenceEngine::DataConfig> outDataConfig;
InferenceEngine::SizeVector order(inpShape.size());
std::iota(order.begin(), order.end(), 0);
// Allow any offset before data
size_t offset((std::numeric_limits<size_t>::max)());
// Input shape
InferenceEngine::DataConfig inpConf;
inpConf.desc = InferenceEngine::TensorDesc(InferenceEngine::Precision::FP32, inpShape, {inpShape, order, offset});
inDataConfig.push_back(inpConf);
// Output shape
InferenceEngine::DataConfig outConf;
outConf.desc = InferenceEngine::TensorDesc(InferenceEngine::Precision::FP32, outShape, {outShape, order, offset});
outDataConfig.push_back(outConf);
InferenceEngine::LayerConfig layerConfig;
layerConfig.inConfs = inDataConfig;
layerConfig.outConfs = outDataConfig;
conf.push_back(layerConfig);
return InferenceEngine::StatusCode::OK;
}
InferenceEngine::StatusCode FFTImpl::init(InferenceEngine::LayerConfig &config, InferenceEngine::ResponseDesc *resp) noexcept {
try {
if (config.inConfs.size() != 1 || config.outConfs.size() != 1) {
THROW_IE_EXCEPTION << "Operation cannot be initialized with incorrect number of inputs/outputs!";
}
if (config.outConfs[0].desc.getPrecision() != InferenceEngine::Precision::FP32 ||
config.inConfs[0].desc.getPrecision() != InferenceEngine::Precision::FP32) {
THROW_IE_EXCEPTION << "Operation supports only FP32 precisions!";
}
} catch (InferenceEngine::details::InferenceEngineException& ex) {
if (resp) {
strncpy(resp->msg, error.c_str(), sizeof(resp->msg) - 1);
resp->msg[sizeof(resp->msg)-1] = 0;
}
return InferenceEngine::GENERAL_ERROR;
}
return InferenceEngine::OK;
}
static cv::Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
{
// NOTE: Inference Engine sizes are reversed.
std::vector<size_t> dims = blob->getTensorDesc().getDims();
std::vector<int> size(dims.begin(), dims.end());
auto precision = blob->getTensorDesc().getPrecision();
CV_Assert(precision == InferenceEngine::Precision::FP32);
return cv::Mat(size, CV_32F, (void*)blob->buffer());
}
InferenceEngine::StatusCode FFTImpl::execute(std::vector<InferenceEngine::Blob::Ptr> &inputs,
std::vector<InferenceEngine::Blob::Ptr> &outputs,
InferenceEngine::ResponseDesc *resp) noexcept {
cv::Mat inp = infEngineBlobToMat(inputs[0]);
cv::Mat out = infEngineBlobToMat(outputs[0]);
const int n = inp.size[0];
const int h = inp.size[2];
const int w = inp.size[3];
cv::Mat complex(h, w, CV_32FC2), interleavedOut(h, w, CV_32FC2);
for (int i = 0; i < n; ++i) {
std::vector<cv::Mat> components = {
cv::Mat(h, w, CV_32F, inp.ptr<float>(i, 0)),
cv::Mat(h, w, CV_32F, inp.ptr<float>(i, 1))
};
cv::merge(components, complex);
if (!inverse)
cv::dft(complex, interleavedOut);
else
cv::idft(complex, interleavedOut, cv::DFT_SCALE);
components = {
cv::Mat(h, w, CV_32F, out.ptr<float>(i, 0)),
cv::Mat(h, w, CV_32F, out.ptr<float>(i, 1))
};
cv::split(interleavedOut, components);
}
return InferenceEngine::OK;
}
//! [fft_kernel:implementation]