[IE TESTS] Added test for NMS (#2830)

2020-11-17 09:33:19 +03:00 · 2020-11-17 09:33:19 +03:00 · a166f95acc
commit a166f95acc
parent 6467a9f5b8
10 changed files with 285 additions and 5 deletions
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/non_max_suppression.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/non_max_suppression.cpp
@ -0,0 +1,42 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "single_layer_tests/non_max_suppression.hpp"
+#include "common_test_utils/test_constants.hpp"
+
+using namespace LayerTestsDefinitions;
+using namespace InferenceEngine;
+using namespace ngraph;
+
+const std::vector<InputShapeParams> inShapeParams = {
+    InputShapeParams{3, 100, 5},
+    InputShapeParams{1, 10, 50},
+    InputShapeParams{2, 50, 50}
+};
+
+const std::vector<int32_t> maxOutBoxPerClass = {5, 20};
+const std::vector<float> threshold = {0.3f, 0.7f};
+const std::vector<float> sigmaThreshold = {0.0f, 0.5f};
+const std::vector<op::v5::NonMaxSuppression::BoxEncodingType> encodType = {op::v5::NonMaxSuppression::BoxEncodingType::CENTER,
+                                                                           op::v5::NonMaxSuppression::BoxEncodingType::CORNER};
+const std::vector<bool> sortResDesc = {true, false};
+const std::vector<element::Type> outType = {element::i32, element::i64};
+
+const auto nmsParams = ::testing::Combine(::testing::ValuesIn(inShapeParams),
+                                          ::testing::Combine(::testing::Values(Precision::FP32),
+                                                             ::testing::Values(Precision::I32),
+                                                             ::testing::Values(Precision::FP32)),
+                                          ::testing::ValuesIn(maxOutBoxPerClass),
+                                          ::testing::ValuesIn(threshold),
+                                          ::testing::ValuesIn(threshold),
+                                          ::testing::ValuesIn(sigmaThreshold),
+                                          ::testing::ValuesIn(encodType),
+                                          ::testing::ValuesIn(sortResDesc),
+                                          ::testing::ValuesIn(outType),
+                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)
+);
+
+INSTANTIATE_TEST_CASE_P(smoke_NmsLayerTest, NmsLayerTest, nmsParams, NmsLayerTest::getTestCaseName);
--- a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/non_max_suppression.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/non_max_suppression.hpp
@ -0,0 +1,48 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <tuple>
+#include <string>
+
+#include "functional_test_utils/layer_test_utils.hpp"
+#include "ngraph_functions/builders.hpp"
+
+namespace LayerTestsDefinitions {
+
+using InputShapeParams = std::tuple<size_t,  // Number of batches
+                                    size_t,  // Number of boxes
+                                    size_t>; // Number of classes
+
+using InputPrecisions = std::tuple<InferenceEngine::Precision,  // boxes and scores precisions
+                                   InferenceEngine::Precision,  // max_output_boxes_per_class precision
+                                   InferenceEngine::Precision>; // iou_threshold, score_threshold, soft_nms_sigma precisions
+
+using NmsParams = std::tuple<InputShapeParams,                                   // Params using to create 1st and 2nd inputs
+                             InputPrecisions,                                    // Input precisions
+                             int32_t,                                            // Max output boxes per class
+                             float,                                              // IOU threshold
+                             float,                                              // Score threshold
+                             float,                                              // Soft NMS sigma
+                             ngraph::op::v5::NonMaxSuppression::BoxEncodingType, // Box encoding
+                             bool,                                               // Sort result descending
+                             ngraph::element::Type,                              // Output type
+                             std::string>;                                       // Device name
+
+class NmsLayerTest : public testing::WithParamInterface<NmsParams>, virtual public LayerTestsUtils::LayerTestsCommon {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<NmsParams> obj);
+    void ConfigureNetwork() override;
+    void Infer() override;
+    void Compare(const std::vector<std::vector<std::uint8_t>> &expectedOutputs, const std::vector<InferenceEngine::Blob::Ptr> &actualOutputs) override;
+
+protected:
+    void SetUp() override;
+
+private:
+    size_t numOfSelectedBoxes;
+};
+
+}  // namespace LayerTestsDefinitions
--- a/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/non_max_suppression.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/non_max_suppression.cpp
@ -0,0 +1,140 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "single_layer_tests/non_max_suppression.hpp"
+
+namespace LayerTestsDefinitions {
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using namespace FuncTestUtils::PrecisionUtils;
+
+std::string NmsLayerTest::getTestCaseName(testing::TestParamInfo<NmsParams> obj) {
+    InputShapeParams inShapeParams;
+    InputPrecisions inPrecisions;
+    int32_t maxOutBoxesPerClass;
+    float iouThr, scoreThr, softNmsSigma;
+    op::v5::NonMaxSuppression::BoxEncodingType boxEncoding;
+    bool sortResDescend;
+    element::Type outType;
+    std::string targetDevice;
+    std::tie(inShapeParams, inPrecisions, maxOutBoxesPerClass, iouThr, scoreThr, softNmsSigma, boxEncoding, sortResDescend, outType, targetDevice) = obj.param;
+
+    size_t numBatches, numBoxes, numClasses;
+    std::tie(numBatches, numBoxes, numClasses) = inShapeParams;
+
+    Precision paramsPrec, maxBoxPrec, thrPrec;
+    std::tie(paramsPrec, maxBoxPrec, thrPrec) = inPrecisions;
+
+    std::ostringstream result;
+    result << "numBatches=" << numBatches << "_numBoxes=" << numBoxes << "_numClasses=" << numClasses << "_";
+    result << "paramsPrec=" << paramsPrec << "_maxBoxPrec=" << maxBoxPrec << "_thrPrec=" << thrPrec << "_";
+    result << "maxOutBoxesPerClass=" << maxOutBoxesPerClass << "_";
+    result << "iouThr=" << iouThr << "_scoreThr=" << scoreThr << "_softNmsSigma=" << softNmsSigma << "_";
+    result << "boxEncoding=" << boxEncoding << "_sortResDescend=" << sortResDescend << "_outType=" << outType << "_";
+    result << "TargetDevice=" << targetDevice;
+    return result.str();
+}
+
+void NmsLayerTest::ConfigureNetwork() {
+    const OutputsDataMap &outputMap = cnnNetwork.getOutputsInfo();
+    auto out = outputMap.begin();
+    for (size_t i = 0; i < outputMap.size(); i++) {
+        if (i < 2) {
+            TensorDesc desc(out->second->getTensorDesc().getPrecision(), SizeVector{numOfSelectedBoxes, 3},
+                            TensorDesc::getLayoutByDims(SizeVector{numOfSelectedBoxes, 3}));
+            *(out->second) = *std::make_shared<Data>(out->first, desc);
+        }
+        out++;
+    }
+}
+
+void NmsLayerTest::Infer() {
+    inferRequest = executableNetwork.CreateInferRequest();
+    inputs.clear();
+
+    size_t it = 0;
+    for (const auto &input : cnnNetwork.getInputsInfo()) {
+        const auto &info = input.second;
+        Blob::Ptr blob;
+
+        if (it == 1) {
+            blob = make_blob_with_precision(info->getTensorDesc());
+            blob->allocate();
+            CommonTestUtils::fill_data_random_float<Precision::FP32>(blob, 1, 0, 1000);
+        } else {
+            blob = GenerateInput(*info);
+        }
+        inferRequest.SetBlob(info->name(), blob);
+        inputs.push_back(blob);
+        it++;
+    }
+    inferRequest.Infer();
+}
+
+void NmsLayerTest::Compare(const std::vector<std::vector<std::uint8_t>> &expectedOutputs, const std::vector<Blob::Ptr> &actualOutputs) {
+    for (int outputIndex = static_cast<int>(expectedOutputs.size()) - 1; outputIndex >=0 ; outputIndex--) {
+        const auto& expected = expectedOutputs[outputIndex];
+        const auto& actual = actualOutputs[outputIndex];
+
+        const auto &expectedBuffer = expected.data();
+        auto memory = as<MemoryBlob>(actual);
+        IE_ASSERT(memory);
+        const auto lockedMemory = memory->wmap();
+        const auto actualBuffer = lockedMemory.as<const uint8_t *>();
+
+        if (outputIndex == 2) {
+            if (expected.size() != actual->byteSize())
+                throw std::runtime_error("Expected and actual size 3rd output have different size");
+        }
+
+        const auto &precision = actual->getTensorDesc().getPrecision();
+        size_t size = expected.size() / actual->getTensorDesc().getPrecision().size();
+        switch (precision) {
+            case Precision::FP32:
+                LayerTestsCommon::Compare(reinterpret_cast<const float *>(expectedBuffer), reinterpret_cast<const float *>(actualBuffer), size, threshold);
+                break;
+            case Precision::I32:
+                LayerTestsCommon::Compare(reinterpret_cast<const int32_t *>(expectedBuffer), reinterpret_cast<const int32_t *>(actualBuffer), size, 0);
+                break;
+            default:
+                FAIL() << "Comparator for " << precision << " precision isn't supported";
+        }
+    }
+}
+
+void NmsLayerTest::SetUp() {
+    InputShapeParams inShapeParams;
+    InputPrecisions inPrecisions;
+    size_t maxOutBoxesPerClass;
+    float iouThr, scoreThr, softNmsSigma;
+    op::v5::NonMaxSuppression::BoxEncodingType boxEncoding;
+    bool sortResDescend;
+    element::Type outType;
+    std::tie(inShapeParams, inPrecisions, maxOutBoxesPerClass, iouThr, scoreThr, softNmsSigma, boxEncoding, sortResDescend, outType,
+             targetDevice) = this->GetParam();
+
+    size_t numBatches, numBoxes, numClasses;
+    std::tie(numBatches, numBoxes, numClasses) = inShapeParams;
+
+    Precision paramsPrec, maxBoxPrec, thrPrec;
+    std::tie(paramsPrec, maxBoxPrec, thrPrec) = inPrecisions;
+
+    numOfSelectedBoxes = std::min(numBoxes, maxOutBoxesPerClass) * numBatches * numClasses;
+
+    const std::vector<size_t> boxesShape{numBatches, numBoxes, 4}, scoresShape{numBatches, numClasses, numBoxes};
+    auto ngPrc = convertIE2nGraphPrc(paramsPrec);
+    auto params = builder::makeParams(ngPrc, {boxesShape, scoresShape});
+    auto paramOuts = helpers::convert2OutputVector(helpers::castOps2Nodes<op::Parameter>(params));
+
+    auto nms = builder::makeNms(paramOuts[0], paramOuts[1], convertIE2nGraphPrc(maxBoxPrec), convertIE2nGraphPrc(thrPrec), maxOutBoxesPerClass, iouThr,
+                                scoreThr, softNmsSigma, boxEncoding, sortResDescend, outType);
+    function = std::make_shared<Function>(nms, params, "NMS");
+}
+
+TEST_P(NmsLayerTest, CompareWithRefs) {
+    Run();
+};
+
+}  // namespace LayerTestsDefinitions
--- a/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.cpp
+++ b/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.cpp
@ -268,7 +268,7 @@ void LayerTestsCommon::Compare(const InferenceEngine::Blob::Ptr &expected, const
    }
 }

-void LayerTestsCommon::ConfigureNetwork() const {
+void LayerTestsCommon::ConfigureNetwork() {
    for (const auto &in : cnnNetwork.getInputsInfo()) {
        if (inLayout != InferenceEngine::Layout::ANY) {
            in.second->setLayout(inLayout);
--- a/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.hpp
+++ b/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.hpp
@ -190,7 +190,7 @@ protected:
        return core;
    }

-    void ConfigureNetwork() const;
+    virtual void ConfigureNetwork();

    void LoadNetwork();

--- a/inference-engine/tests/ngraph_functions/include/ngraph_functions/builders.hpp
+++ b/inference-engine/tests/ngraph_functions/include/ngraph_functions/builders.hpp
@ -448,5 +448,17 @@ std::shared_ptr<ngraph::Node> makeNormalizeL2(const ngraph::Output<Node>& data,
                                              float eps,
                                              ngraph::op::EpsMode epsMode);

+std::shared_ptr<ngraph::Node> makeNms(const ngraph::Output<Node> &boxes,
+                                      const ngraph::Output<Node> &scores,
+                                      const element::Type& maxBoxesPrec,
+                                      const element::Type& thrPrec,
+                                      const int32_t &maxOutBoxesPerClass,
+                                      const float &iouThr,
+                                      const float &scoreThr,
+                                      const float &softNmsSigma,
+                                      const ngraph::op::v5::NonMaxSuppression::BoxEncodingType &boxEncoding,
+                                      const bool &sortResDescend,
+                                      const ngraph::element::Type& outType);
+
 }  // namespace builder
 }  // namespace ngraph
--- a/inference-engine/tests/ngraph_functions/include/ngraph_functions/utils/ngraph_helpers.hpp
+++ b/inference-engine/tests/ngraph_functions/include/ngraph_functions/utils/ngraph_helpers.hpp
@ -232,7 +232,7 @@ inline ngraph::NodeVector castOps2Nodes(const std::vector<std::shared_ptr<opType

 std::vector<std::vector<std::uint8_t>> interpreterFunction(const std::shared_ptr<Function> &function,
                                                           const std::vector<std::vector<std::uint8_t>> &inputs,
-                                                           std::vector<ngraph::element::Type_t> convertType = {});
+                                                           const std::vector<ngraph::element::Type_t> convertType = {});

 //
 // This function compares two nGraph functions and requires them to have exactly one output
--- a/inference-engine/tests/ngraph_functions/src/non_max_suppression.cpp
+++ b/inference-engine/tests/ngraph_functions/src/non_max_suppression.cpp
@ -0,0 +1,31 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph_functions/builders.hpp"
+
+namespace ngraph {
+namespace builder {
+
+std::shared_ptr<ngraph::Node> makeNms(const ngraph::Output<Node> &boxes,
+                                      const ngraph::Output<Node> &scores,
+                                      const element::Type& maxBoxesPrec,
+                                      const element::Type& thrPrec,
+                                      const int32_t &maxOutBoxesPerClass,
+                                      const float &iouThr,
+                                      const float &scoreThr,
+                                      const float &softNmsSigma,
+                                      const ngraph::op::v5::NonMaxSuppression::BoxEncodingType &boxEncoding,
+                                      const bool &sortResDescend,
+                                      const ngraph::element::Type& outType) {
+    auto maxOutBoxesPerClassNode = makeConstant(maxBoxesPrec, ngraph::Shape{}, std::vector<int32_t>{maxOutBoxesPerClass})->output(0);
+    auto iouThrNode = makeConstant(thrPrec, ngraph::Shape{}, std::vector<float>{iouThr})->output(0);
+    auto scoreThrNode = makeConstant(thrPrec, ngraph::Shape{}, std::vector<float>{scoreThr})->output(0);
+    auto softNmsSigmaNode = makeConstant(thrPrec, ngraph::Shape{}, std::vector<float>{softNmsSigma})->output(0);
+
+    return std::make_shared<ngraph::op::v5::NonMaxSuppression>(boxes, scores, maxOutBoxesPerClassNode, iouThrNode, scoreThrNode, softNmsSigmaNode,
+                                                               boxEncoding, sortResDescend, outType);
+}
+
+}  // namespace builder
+}  // namespace ngraph
--- a/inference-engine/tests/ngraph_functions/src/utils/ngraph_helpers.cpp
+++ b/inference-engine/tests/ngraph_functions/src/utils/ngraph_helpers.cpp
@ -77,7 +77,7 @@ OutputVector convert2OutputVector(const std::vector<std::shared_ptr<Node>> &node
 }

 std::vector<std::vector<std::uint8_t>> interpreterFunction(const std::shared_ptr<Function> &function, const std::vector<std::vector<std::uint8_t>> &inputs,
-                                                           std::vector<ngraph::element::Type_t> convertType) {
+                                                           const std::vector<ngraph::element::Type_t> convertType) {
    runtime::Backend::set_backend_shared_library_search_directory("");
    auto backend = runtime::Backend::create("INTERPRETER");

--- a/ngraph/core/reference/src/runtime/reference/non_max_suppression.cpp
+++ b/ngraph/core/reference/src/runtime/reference/non_max_suppression.cpp
@ -262,7 +262,14 @@ namespace ngraph
                {
                    std::sort(filteredBoxes.begin(),
                              filteredBoxes.end(),
-                              [](const BoxInfo& l, const BoxInfo& r) { return l.score > r.score; });
+                              [](const BoxInfo& l, const BoxInfo& r) {
+                                  return (l.score > r.score) ||
+                                         (l.score == r.score && l.batch_index < r.batch_index) ||
+                                         (l.score == r.score && l.batch_index == r.batch_index &&
+                                          l.class_index < r.class_index) ||
+                                         (l.score == r.score && l.batch_index == r.batch_index &&
+                                          l.class_index == r.class_index && l.index < r.index);
+                              });
                }

                size_t max_num_of_selected_indices = selected_indices_shape[0];