Merge remote-tracking branch 'upstream/master' into revise_op_select

rnugmanx · Jul 12, 2021 · c2dd8f3 · c2dd8f3
2 parents e28f40c + 811f4c5
commit c2dd8f3
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Showing 38 changed files with 1,276 additions and 1,995 deletions.
diff --git a/.ci/azure/linux.yml b/.ci/azure/linux.yml
@@ -103,7 +103,6 @@ jobs:
       cmakeArgs: >
         -GNinja
         -DVERBOSE_BUILD=ON
-        -DENABLE_TEMPLATE_PLUGIN=ON
         -DCMAKE_BUILD_TYPE=$(BUILD_TYPE)
         -DENABLE_PYTHON=ON
         -DPYTHON_EXECUTABLE=/usr/bin/python3.6

diff --git a/cmake/features.cmake b/cmake/features.cmake
@@ -24,8 +24,6 @@ Supported values:\
 
 ie_option (ENABLE_PROFILING_FIRST_INFERENCE "Build with ITT tracing of first inference time." ON)
 
-ie_option(ENABLE_TEMPLATE_PLUGIN "Register template plugin into plugins.xml" OFF)
-
 ie_option_enum(SELECTIVE_BUILD "Enable OpenVINO conditional compilation or statistics collection. \
 In case SELECTIVE_BUILD is enabled, the SELECTIVE_BUILD_STAT variable should contain the path to the collected InelSEAPI statistics. \
 Usage: -DSELECTIVE_BUILD=ON -DSELECTIVE_BUILD_STAT=/path/*.csv" OFF

diff --git a/docs/template_plugin/src/CMakeLists.txt b/docs/template_plugin/src/CMakeLists.txt
@@ -36,10 +36,8 @@ target_link_libraries(${TARGET_NAME} PRIVATE
 set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
 # ATTENTION: uncomment to register a plugin in the plugins.xml file
-if(ENABLE_TEMPLATE_PLUGIN)
-    ie_register_plugins(MAIN_TARGET ${TARGET_NAME}
-                        POSSIBLE_PLUGINS ${TARGET_NAME})
-endif()
+# ie_register_plugins(MAIN_TARGET ${TARGET_NAME}
+#                     POSSIBLE_PLUGINS ${TARGET_NAME})
 # [cmake:plugin]
 
 # ATTENTION: uncomment to install component

diff --git a/docs/template_plugin/src/template_plugin.cpp b/docs/template_plugin/src/template_plugin.cpp
@@ -66,8 +66,17 @@ std::shared_ptr<ngraph::Function> TransformNetwork(const std::shared_ptr<const n
     // TODO: add post-processing based on outputsInfoMap
     // Example: register CommonOptimizations transformation from transformations library
     passManager.register_pass<ngraph::pass::CommonOptimizations>();
-    // Template plugin handles only FP32 networks
-    passManager.register_pass<ngraph::pass::ConvertPrecision>(precisions_array {{ngraph::element::f16, ngraph::element::f32}});
+    // GAPI supports only FP32 networks for pre-processing
+    bool needF16toF32 = false;
+    for (const auto& param : function->get_parameters()) {
+        if (param->get_element_type() == ngraph::element::f16 &&
+            inputInfoMap.at(param->get_friendly_name())->getTensorDesc().getPrecision() != InferenceEngine::Precision::FP16) {
+            needF16toF32 = true;
+            break;
+        }
+    }
+    if (needF16toF32)
+        passManager.register_pass<ngraph::pass::ConvertPrecision>(precisions_array {{ngraph::element::f16, ngraph::element::f32}});
     // Example: register plugin specific transformation
     passManager.register_pass<ngraph::pass::DecomposeDivideMatcher>();
     passManager.register_pass<ngraph::pass::ReluReluFusionMatcher>();

diff --git a/docs/template_plugin/tests/functional/op_reference/base_reference_test.cpp b/docs/template_plugin/tests/functional/op_reference/base_reference_test.cpp
@@ -0,0 +1,173 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "base_reference_test.hpp"
+
+#include <gtest/gtest.h>
+
+#include "transformations/utils/utils.hpp"
+
+using namespace InferenceEngine;
+
+CommonReferenceTest::CommonReferenceTest(): targetDevice("TEMPLATE") {
+    core = PluginCache::get().ie(targetDevice);
+}
+
+void CommonReferenceTest::Exec() {
+    LoadNetwork();
+    FillInputs();
+    Infer();
+    Validate();
+}
+
+void CommonReferenceTest::LoadNetwork() {
+    InferenceEngine::CNNNetwork cnnNetwork(function);
+    auto inputInfo = cnnNetwork.getInputsInfo();
+    auto outputInfo = cnnNetwork.getOutputsInfo();
+    for (const auto& param : function->get_parameters()) {
+        inputInfo[param->get_friendly_name()]->setPrecision(InferenceEngine::details::convertPrecision(param->get_element_type()));
+    }
+    for (const auto& result : function->get_results()) {
+        outputInfo[ngraph::op::util::create_ie_output_name(result->input_value(0))]->setPrecision(
+            InferenceEngine::details::convertPrecision(result->get_element_type()));
+    }
+    executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice);
+}
+
+void CommonReferenceTest::FillInputs() {
+    const auto& inputInfo = executableNetwork.GetInputsInfo();
+    const auto& params = function->get_parameters();
+    ASSERT_EQ(params.size(), inputData.size());
+    ASSERT_EQ(inputInfo.size(), inputData.size());
+
+    for (size_t i = 0; i < params.size(); i++) {
+        const auto& param = params[i];
+        const auto infoIt = inputInfo.find(param->get_friendly_name());
+        GTEST_ASSERT_NE(infoIt, inputInfo.cend());
+
+        const auto& info = infoIt->second;
+        auto blob = make_blob_with_precision(info->getTensorDesc());
+        blob->allocate();
+
+        ASSERT_EQ(blob->byteSize(), inputData[i]->byteSize());
+
+        MemoryBlob::Ptr mInputData = as<MemoryBlob>(inputData[i]);
+        ASSERT_NE(mInputData, nullptr);
+        auto minputDataHolder = mInputData->rmap();
+
+        MemoryBlob::Ptr mBlob = as<MemoryBlob>(blob);
+        ASSERT_NE(mBlob, nullptr);
+        auto mBlobHolder = mBlob->wmap();
+
+        std::memcpy(mBlobHolder.as<void*>(), minputDataHolder.as<const void*>(), inputData[i]->byteSize());
+        inputData[i] = blob;
+    }
+}
+
+void CommonReferenceTest::Infer() {
+    inferRequest = executableNetwork.CreateInferRequest();
+
+    const auto& inputsInfo = executableNetwork.GetInputsInfo();
+    const auto& functionParams = function->get_parameters();
+    for (size_t i = 0; i < functionParams.size(); ++i) {
+        const auto& param = functionParams[i];
+        const auto infoIt = inputsInfo.find(param->get_friendly_name());
+        GTEST_ASSERT_NE(infoIt, inputsInfo.cend());
+
+        const auto& info = infoIt->second;
+        auto blob = inputData[i];
+
+        inferRequest.SetBlob(info->name(), blob);
+    }
+    inferRequest.Infer();
+}
+
+void CommonReferenceTest::Validate() {
+    ASSERT_EQ(executableNetwork.GetOutputsInfo().size(), refOutData.size());
+    std::vector<InferenceEngine::Blob::Ptr> outputs;
+    for (const auto& result : function->get_results()) {
+        auto name = ngraph::op::util::create_ie_output_name(result->input_value(0));
+        outputs.emplace_back(inferRequest.GetBlob(name));
+    }
+
+    ASSERT_EQ(refOutData.size(), outputs.size());
+    for (size_t i = 0; i < refOutData.size(); i++) {
+        ValidateBlobs(refOutData[i], outputs[i]);
+    }
+}
+void CommonReferenceTest::ValidateBlobs(const InferenceEngine::Blob::Ptr& refBlob, const InferenceEngine::Blob::Ptr& outBlob) {
+    ASSERT_TRUE(refBlob != nullptr);
+    ASSERT_TRUE(outBlob != nullptr);
+    ASSERT_EQ(refBlob->getTensorDesc().getPrecision(), outBlob->getTensorDesc().getPrecision());
+    ASSERT_EQ(refBlob->byteSize(), outBlob->byteSize());
+
+    auto mRef = as<InferenceEngine::MemoryBlob>(refBlob);
+    IE_ASSERT(mRef);
+    const auto refLockMemory = mRef->rmap();
+    const auto refBuffer = refLockMemory.as<const std::uint8_t*>();
+
+    auto mOut = as<InferenceEngine::MemoryBlob>(outBlob);
+    IE_ASSERT(mOut);
+    const auto outLockMemory = mOut->rmap();
+    const auto outBuffer = outLockMemory.as<const std::uint8_t*>();
+
+    const auto& precision = refBlob->getTensorDesc().getPrecision();
+    switch (precision) {
+    case InferenceEngine::Precision::BF16:
+        LayerTestsUtils::LayerTestsCommon::Compare<ngraph::bfloat16, ngraph::bfloat16>(
+            reinterpret_cast<const ngraph::bfloat16*>(refBuffer), reinterpret_cast<const ngraph::bfloat16*>(outBuffer), refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::FP16:
+        LayerTestsUtils::LayerTestsCommon::Compare<ngraph::float16, ngraph::float16>(
+            reinterpret_cast<const ngraph::float16*>(refBuffer), reinterpret_cast<const ngraph::float16*>(outBuffer), refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::FP32:
+        LayerTestsUtils::LayerTestsCommon::Compare<float, float>(reinterpret_cast<const float*>(refBuffer), reinterpret_cast<const float*>(outBuffer),
+                                                                 refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::I8:
+        LayerTestsUtils::LayerTestsCommon::Compare<int8_t, int8_t>(reinterpret_cast<const int8_t*>(refBuffer), reinterpret_cast<const int8_t*>(outBuffer),
+                                                                   refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::I16:
+        LayerTestsUtils::LayerTestsCommon::Compare<int16_t, int16_t>(reinterpret_cast<const int16_t*>(refBuffer), reinterpret_cast<const int16_t*>(outBuffer),
+                                                                     refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::I32:
+        LayerTestsUtils::LayerTestsCommon::Compare<int32_t, int32_t>(reinterpret_cast<const int32_t*>(refBuffer), reinterpret_cast<const int32_t*>(outBuffer),
+                                                                     refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::I64:
+        LayerTestsUtils::LayerTestsCommon::Compare<int64_t, int64_t>(reinterpret_cast<const int64_t*>(refBuffer), reinterpret_cast<const int64_t*>(outBuffer),
+                                                                     refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::BOOL:
+    case InferenceEngine::Precision::U8:
+        LayerTestsUtils::LayerTestsCommon::Compare<uint8_t, uint8_t>(reinterpret_cast<const uint8_t*>(refBuffer), reinterpret_cast<const uint8_t*>(outBuffer),
+                                                                     refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::U16:
+        LayerTestsUtils::LayerTestsCommon::Compare<uint16_t, uint16_t>(reinterpret_cast<const uint16_t*>(refBuffer),
+                                                                       reinterpret_cast<const uint16_t*>(outBuffer), refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::U32:
+        LayerTestsUtils::LayerTestsCommon::Compare<uint32_t, uint32_t>(reinterpret_cast<const uint32_t*>(refBuffer),
+                                                                       reinterpret_cast<const uint32_t*>(outBuffer), refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::U64:
+        LayerTestsUtils::LayerTestsCommon::Compare<uint64_t, uint64_t>(reinterpret_cast<const uint64_t*>(refBuffer),
+                                                                       reinterpret_cast<const uint64_t*>(outBuffer), refBlob->size(), threshold);
+        break;
+    case InferenceEngine::Precision::I4:
+    case InferenceEngine::Precision::U4:
+        LayerTestsUtils::LayerTestsCommon::Compare<uint8_t, uint8_t>(reinterpret_cast<const uint8_t*>(refBuffer), reinterpret_cast<const uint8_t*>(outBuffer),
+                                                                     refBlob->size() / 2, threshold);
+        break;
+    case InferenceEngine::Precision::BIN:
+        LayerTestsUtils::LayerTestsCommon::Compare<uint8_t, uint8_t>(reinterpret_cast<const uint8_t*>(refBuffer), reinterpret_cast<const uint8_t*>(outBuffer),
+                                                                     refBlob->size() / 8, threshold);
+        break;
+    default:
+        FAIL() << "Comparator for " << precision << " precision isn't supported";
+    }
+}
diff --git a/docs/template_plugin/tests/functional/op_reference/base_reference_test.hpp b/docs/template_plugin/tests/functional/op_reference/base_reference_test.hpp
@@ -0,0 +1,53 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <ie_core.hpp>
+#include <ie_ngraph_utils.hpp>
+#include <ngraph/ngraph.hpp>
+#include <shared_test_classes/base/layer_test_utils.hpp>
+
+class CommonReferenceTest {
+public:
+    CommonReferenceTest();
+
+    void Exec();
+
+    void LoadNetwork();
+
+    void FillInputs();
+
+    void Infer();
+
+    void Validate();
+
+private:
+    void ValidateBlobs(const InferenceEngine::Blob::Ptr& refBlob, const InferenceEngine::Blob::Ptr& outBlob);
+
+protected:
+    const std::string targetDevice;
+    std::shared_ptr<InferenceEngine::Core> core;
+    std::shared_ptr<ngraph::Function> function;
+
+    InferenceEngine::ExecutableNetwork executableNetwork;
+    InferenceEngine::InferRequest inferRequest;
+    std::vector<InferenceEngine::Blob::Ptr> inputData;
+    std::vector<InferenceEngine::Blob::Ptr> refOutData;
+    float threshold = 1e-2f;
+};
+
+template <class T>
+InferenceEngine::Blob::Ptr CreateBlob(const ngraph::element::Type& element_type, const std::vector<T>& values, size_t size = 0) {
+    size_t real_size = size ? size : values.size() * sizeof(T) / element_type.size();
+    auto blob = make_blob_with_precision(
+        InferenceEngine::TensorDesc(InferenceEngine::details::convertPrecision(element_type), {real_size}, InferenceEngine::Layout::C));
+    blob->allocate();
+    InferenceEngine::MemoryBlob::Ptr minput = InferenceEngine::as<InferenceEngine::MemoryBlob>(blob);
+    IE_ASSERT(minput);
+    auto minputHolder = minput->wmap();
+
+    std::memcpy(minputHolder.as<void*>(), values.data(), std::min(real_size * element_type.size(), sizeof(T) * values.size()));
+
+    return blob;
+}
+