8b1b900591
* Fixed preprocessing for template plugin * Added more tests instances * Split common transformation to smaller ones which can be used by plugins * Moved preprocessing transformation to Plugin API * Added PreprocessConversionTest tests * Disabled tests on GPU: CVS-51764 * Disabled some tests on VPU and TEMPLATE * Support for input layout conversions in TEMPLATE plugin * Improvements in Template Plugin * Fixed compilation * Fixes * Disables some tests * Fixed compilation on Windows * Fixed docs
102 lines
3.9 KiB
C++
102 lines
3.9 KiB
C++
// Copyright (C) 2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <ngraph/pass/manager.hpp>
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#include <ngraph/opsets/opset3.hpp>
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#include "transformations/preprocessing/mean_image_or_value.hpp"
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#include "transformations/preprocessing/std_scale.hpp"
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#include "transformations/preprocessing/preprocessing.hpp"
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NGRAPH_RTTI_DEFINITION(ngraph::pass::AddPreprocessing, "AddPreprocessing", 0);
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ngraph::pass::AddPreprocessing::AddPreprocessing(const InferenceEngine::InputsDataMap & inputInfoMap)
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: m_inputInfoMap(inputInfoMap) { }
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bool ngraph::pass::AddPreprocessing::run_on_function(std::shared_ptr<ngraph::Function> f) {
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ngraph::pass::AddMeanSubtract::MeanMap meanMap;
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ngraph::pass::AddStdScale::ScaleMap scaleMap;
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for (const auto & it : m_inputInfoMap) {
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bool has_scales = false, has_mean_values = false, has_mean_image = false;
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const InferenceEngine::PreProcessInfo & pInfo = it.second->getPreProcess();
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const auto & inputDims = it.second->getTensorDesc().getDims();
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const size_t cn = pInfo.getNumberOfChannels();
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std::vector<float> meanValues(cn), stdScales(cn);
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InferenceEngine::Blob::Ptr meanImage = nullptr;
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for (size_t c = 0; c < cn; ++c) {
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if ((stdScales[c] = pInfo[c]->stdScale) != 1.0f) {
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has_scales = true;
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}
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if ((meanValues[c] = pInfo[c]->meanValue) != 0.0f) {
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has_mean_values = true;
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}
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if (pInfo[c]->meanData != nullptr) {
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has_mean_image = true;
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if (c == 0) {
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meanImage = pInfo[c]->meanData;
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NGRAPH_CHECK(meanImage->getTensorDesc().getPrecision() == InferenceEngine::Precision::FP32,
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"Only InferenceEngine::Precision::FP32 precision is supported for PreProcessChannel::meanData");
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} else {
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NGRAPH_CHECK(meanImage->getTensorDesc() == pInfo[c]->meanData->getTensorDesc(),
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"TensorDesc for PreProcessChannel::meanData must be equal");
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}
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}
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}
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// no preprocessing for current input
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if (!has_mean_values && !has_scales && !has_mean_image) {
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continue;
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}
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NGRAPH_CHECK(!(has_mean_image && has_scales),
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"Only PreProcessChannel::meanData or PreProcessChannel::meanValue can be set.");
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if (has_scales) {
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ngraph::Shape shape(inputDims.size(), 1);
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shape[1] = stdScales.size(); // C
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scaleMap[it.first] = ngraph::opset3::Constant::create(ngraph::element::f32, shape, stdScales);
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}
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if (has_mean_values) {
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ngraph::Shape shape(inputDims.size(), 1);
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shape[1] = meanValues.size(); // C
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meanMap[it.first] = ngraph::opset3::Constant::create(ngraph::element::f32, shape, meanValues);
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} else if (has_mean_image) {
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ngraph::Shape shape = { cn };
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auto dims = meanImage->getTensorDesc().getDims();
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std::copy(dims.begin(), dims.end(), std::back_inserter(shape));
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std::vector<float> meanImageData(ngraph::shape_size(shape));
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for (size_t c = 0, i = 0; c < cn; ++c) {
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auto lm = pInfo[c]->meanData->buffer();
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const float *data = lm.as<const float *>();
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std::memcpy(&meanImageData[i], data, meanImage->byteSize());
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i += meanImage->size();
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}
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meanMap[it.first] = ngraph::opset3::Constant::create(ngraph::element::f32,
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shape, meanImageData);
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}
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}
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ngraph::pass::Manager manager(get_pass_config());
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auto preproc = manager.register_pass<ngraph::pass::GraphRewrite>();
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if (!scaleMap.empty()) {
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preproc->add_matcher<ngraph::pass::AddStdScale>(scaleMap);
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}
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if (!meanMap.empty()) {
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preproc->add_matcher<ngraph::pass::AddMeanSubtract>(meanMap);
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}
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manager.run_passes(f);
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return false;
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}
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