Add `fillBlobs` to fix issue with negative values in `image_info` input layer (#3335)

diff --git a/tests/stress_tests/common/ie_pipelines/pipelines.cpp b/tests/stress_tests/common/ie_pipelines/pipelines.cpp
index 4a73a04..c31c636 100644 (file)
--- a/tests/stress_tests/common/ie_pipelines/pipelines.cpp
+++ b/tests/stress_tests/common/ie_pipelines/pipelines.cpp
@@ -4,6 +4,7 @@
 
 #include "pipelines.h"
 #include "../utils.h"
+#include "../ie_utils.h"
 
 #include <iostream>
 #include <string>
@@ -113,6 +114,12 @@ std::function<void()> infer_request_inference(const std::string &model, const st
         CNNNetwork cnnNetwork = ie.ReadNetwork(model);
         ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, target_device);
         InferRequest infer_request = exeNetwork.CreateInferRequest();
+
+        auto batchSize = cnnNetwork.getBatchSize();
+        batchSize = batchSize != 0 ? batchSize : 1;
+        const InferenceEngine::ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+        fillBlobs(infer_request, inputsInfo, batchSize);
+
         infer_request.Infer();
         OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
         for (auto &output : output_info)
@@ -120,10 +127,9 @@ std::function<void()> infer_request_inference(const std::string &model, const st
     };
 }
 
-std::function<void()> reinfer_request_inference(InferenceEngine::InferRequest& infer_request, InferenceEngine::CNNNetwork& cnnNetwork) {
+std::function<void()> reinfer_request_inference(InferenceEngine::InferRequest& infer_request, InferenceEngine::OutputsDataMap& output_info) {
     return [&] {
         infer_request.Infer();
-        OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
         for (auto &output : output_info)
             Blob::Ptr outputBlob = infer_request.GetBlob(output.first);
     };

diff --git a/tests/stress_tests/common/ie_pipelines/pipelines.h b/tests/stress_tests/common/ie_pipelines/pipelines.h
index f34a11c..c474d83 100644 (file)
--- a/tests/stress_tests/common/ie_pipelines/pipelines.h
+++ b/tests/stress_tests/common/ie_pipelines/pipelines.h
@@ -15,5 +15,4 @@ std::function<void()> recreate_exenetwork(InferenceEngine::Core &ie, const std::
 std::function<void()> create_infer_request(const std::string &model, const std::string &target_device);
 std::function<void()> recreate_infer_request(InferenceEngine::ExecutableNetwork& exeNetwork);
 std::function<void()> infer_request_inference(const std::string &model, const std::string &target_device);
-std::function<void()> infer_request_inference(const std::string &model, const std::string &target_device);
-std::function<void()> reinfer_request_inference(InferenceEngine::InferRequest& infer_request, InferenceEngine::CNNNetwork& cnnNetwork);
+std::function<void()> reinfer_request_inference(InferenceEngine::InferRequest& infer_request, InferenceEngine::OutputsDataMap& output_info);

diff --git a/tests/stress_tests/common/ie_utils.cpp b/tests/stress_tests/common/ie_utils.cpp
new file mode 100644 (file)
index 0000000..9909e76
--- /dev/null
+++ b/tests/stress_tests/common/ie_utils.cpp
@@ -0,0 +1,58 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ie_utils.h"
+
+#include <inference_engine.hpp>
+
+using namespace InferenceEngine;
+
+/**
+ * @brief Fill InferRequest blobs with random values or image information
+ */
+void fillBlobs(InferenceEngine::InferRequest inferRequest,
+        const InferenceEngine::ConstInputsDataMap& inputsInfo,
+        const size_t& batchSize) {
+  std::vector<std::pair<size_t, size_t>> input_image_sizes;
+  for (const ConstInputsDataMap::value_type& item : inputsInfo) {
+    if (isImage(item.second))
+      input_image_sizes.push_back(getTensorHeightWidth(item.second->getTensorDesc()));
+  }
+
+  for (const ConstInputsDataMap::value_type& item : inputsInfo) {
+    Blob::Ptr inputBlob = inferRequest.GetBlob(item.first);
+    if (isImageInfo(inputBlob) && (input_image_sizes.size() == 1)) {
+      // Fill image information
+      auto image_size = input_image_sizes.at(0);
+      if (item.second->getPrecision() == InferenceEngine::Precision::FP32) {
+        fillBlobImInfo<float>(inputBlob, batchSize, image_size);
+      } else if (item.second->getPrecision() == InferenceEngine::Precision::FP16) {
+        fillBlobImInfo<short>(inputBlob, batchSize, image_size);
+      } else if (item.second->getPrecision() == InferenceEngine::Precision::I32) {
+        fillBlobImInfo<int32_t>(inputBlob, batchSize, image_size);
+      } else {
+        THROW_IE_EXCEPTION << "Input precision is not supported for image info!";
+      }
+      continue;
+    }
+    // Fill random
+    if (item.second->getPrecision() == InferenceEngine::Precision::FP32) {
+      fillBlobRandom<float>(inputBlob);
+    } else if (item.second->getPrecision() == InferenceEngine::Precision::FP16) {
+      fillBlobRandom<short>(inputBlob);
+    } else if (item.second->getPrecision() == InferenceEngine::Precision::I32) {
+      fillBlobRandom<int32_t>(inputBlob);
+    } else if (item.second->getPrecision() == InferenceEngine::Precision::U8) {
+      fillBlobRandom<uint8_t>(inputBlob);
+    } else if (item.second->getPrecision() == InferenceEngine::Precision::I8) {
+      fillBlobRandom<int8_t>(inputBlob);
+    } else if (item.second->getPrecision() == InferenceEngine::Precision::U16) {
+      fillBlobRandom<uint16_t>(inputBlob);
+    } else if (item.second->getPrecision() == InferenceEngine::Precision::I16) {
+      fillBlobRandom<int16_t>(inputBlob);
+    } else {
+      THROW_IE_EXCEPTION << "Input precision is not supported for " << item.first;
+    }
+  }
+}
\ No newline at end of file

diff --git a/tests/stress_tests/common/ie_utils.h b/tests/stress_tests/common/ie_utils.h
new file mode 100644 (file)
index 0000000..c9df6d5
--- /dev/null
+++ b/tests/stress_tests/common/ie_utils.h
@@ -0,0 +1,114 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <inference_engine.hpp>
+
+using namespace InferenceEngine;
+
+/**
+ * @brief Determine if InferenceEngine blob means image or not
+ */
+template<typename T>
+static bool isImage(const T &blob) {
+  auto descriptor = blob->getTensorDesc();
+  if (descriptor.getLayout() != InferenceEngine::NCHW) {
+    return false;
+  }
+  auto channels = descriptor.getDims()[1];
+  return channels == 3;
+}
+
+
+/**
+ * @brief Determine if InferenceEngine blob means image information or not
+ */
+template<typename T>
+static bool isImageInfo(const T &blob) {
+  auto descriptor = blob->getTensorDesc();
+  if (descriptor.getLayout() != InferenceEngine::NC) {
+    return false;
+  }
+  auto channels = descriptor.getDims()[1];
+  return (channels >= 2);
+}
+
+
+/**
+ * @brief Return height and width from provided InferenceEngine tensor description
+ */
+inline std::pair<size_t, size_t> getTensorHeightWidth(const InferenceEngine::TensorDesc& desc) {
+  const auto& layout = desc.getLayout();
+  const auto& dims = desc.getDims();
+  const auto& size = dims.size();
+  if ((size >= 2) &&
+    (layout == InferenceEngine::Layout::NCHW  ||
+     layout == InferenceEngine::Layout::NHWC  ||
+     layout == InferenceEngine::Layout::NCDHW ||
+     layout == InferenceEngine::Layout::NDHWC ||
+     layout == InferenceEngine::Layout::OIHW  ||
+     layout == InferenceEngine::Layout::GOIHW ||
+     layout == InferenceEngine::Layout::OIDHW ||
+     layout == InferenceEngine::Layout::GOIDHW ||
+     layout == InferenceEngine::Layout::CHW  ||
+     layout == InferenceEngine::Layout::HW)) {
+    // Regardless of layout, dimensions are stored in fixed order
+    return std::make_pair(dims.back(), dims.at(size - 2));
+  } else {
+    THROW_IE_EXCEPTION << "Tensor does not have height and width dimensions";
+  }
+}
+
+
+/**
+ * @brief Fill InferenceEngine blob with random values
+ */
+template<typename T>
+void fillBlobRandom(Blob::Ptr& inputBlob) {
+  MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
+  // locked memory holder should be alive all time while access to its buffer happens
+  auto minputHolder = minput->wmap();
+
+  auto inputBlobData = minputHolder.as<T *>();
+  for (size_t i = 0; i < inputBlob->size(); i++) {
+    auto rand_max = RAND_MAX;
+    inputBlobData[i] = (T) rand() / static_cast<T>(rand_max) * 10;
+  }
+}
+
+
+/**
+ * @brief Fill InferenceEngine blob with image information
+ */
+template<typename T>
+void fillBlobImInfo(Blob::Ptr& inputBlob,
+          const size_t& batchSize,
+          std::pair<size_t, size_t> image_size) {
+  MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
+  // locked memory holder should be alive all time while access to its buffer happens
+  auto minputHolder = minput->wmap();
+
+  auto inputBlobData = minputHolder.as<T *>();
+  for (size_t b = 0; b < batchSize; b++) {
+    size_t iminfoSize = inputBlob->size()/batchSize;
+    for (size_t i = 0; i < iminfoSize; i++) {
+      size_t index = b*iminfoSize + i;
+      if (0 == i)
+        inputBlobData[index] = static_cast<T>(image_size.first);
+      else if (1 == i)
+        inputBlobData[index] = static_cast<T>(image_size.second);
+      else
+        inputBlobData[index] = 1;
+    }
+  }
+}
+
+
+/**
+ * @brief Fill InferRequest blobs with random values or image information
+ */
+void fillBlobs(InferenceEngine::InferRequest inferRequest,
+        const InferenceEngine::ConstInputsDataMap& inputsInfo,
+        const size_t& batchSize);
\ No newline at end of file

diff --git a/tests/stress_tests/memcheck_tests/tests.cpp b/tests/stress_tests/memcheck_tests/tests.cpp
index b21c584..1cdfa9f 100644 (file)
--- a/tests/stress_tests/memcheck_tests/tests.cpp
+++ b/tests/stress_tests/memcheck_tests/tests.cpp
@@ -4,6 +4,7 @@
 
 #include "tests_utils.h"
 #include "../common/tests_utils.h"
+#include "../common/ie_utils.h"
 #include "../common/managers/thread_manager.h"
 #include "tests_pipelines/tests_pipelines.h"
 
@@ -75,6 +76,12 @@ TEST_P(MemCheckTestSuite, infer_request_inference) {
         CNNNetwork cnnNetwork = ie.ReadNetwork(model);
         ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, device);
         InferRequest inferRequest = exeNetwork.CreateInferRequest();
+
+        auto batchSize = cnnNetwork.getBatchSize();
+        batchSize = batchSize != 0 ? batchSize : 1;
+        const ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+        fillBlobs(inferRequest, inputsInfo, batchSize);
+
         inferRequest.Infer();
         OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
         for (auto &output : output_info)

diff --git a/tests/stress_tests/memleaks_tests/tests.cpp b/tests/stress_tests/memleaks_tests/tests.cpp
index 52e0683..599c957 100644 (file)
--- a/tests/stress_tests/memleaks_tests/tests.cpp
+++ b/tests/stress_tests/memleaks_tests/tests.cpp
@@ -3,6 +3,7 @@
 //
 
 #include "../common/tests_utils.h"
+#include "../common/ie_utils.h"
 #include "../common/managers/thread_manager.h"
 #include "tests_pipelines/tests_pipelines.h"
 
@@ -98,7 +99,14 @@ TEST_P(MemLeaksTestSuite, reinfer_request_inference) {
         CNNNetwork cnnNetwork = ie.ReadNetwork(test_params.model);
         ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, test_params.device);
         InferRequest infer_request = exeNetwork.CreateInferRequest();
-        return test_reinfer_request_inference(infer_request, cnnNetwork, test_params.model, test_params.device, test_params.numiters);
+
+        OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
+        auto batchSize = cnnNetwork.getBatchSize();
+        batchSize = batchSize != 0 ? batchSize : 1;
+        const InferenceEngine::ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+        fillBlobs(infer_request, inputsInfo, batchSize);
+
+        return test_reinfer_request_inference(infer_request, output_info, test_params.model, test_params.device, test_params.numiters);
     };
     test_runner(test_params.numthreads, test);
 }

diff --git a/tests/stress_tests/memleaks_tests/tests_pipelines/tests_pipelines.cpp b/tests/stress_tests/memleaks_tests/tests_pipelines/tests_pipelines.cpp
index 8d39d7d..a7cd6d7 100644 (file)
--- a/tests/stress_tests/memleaks_tests/tests_pipelines/tests_pipelines.cpp
+++ b/tests/stress_tests/memleaks_tests/tests_pipelines/tests_pipelines.cpp
@@ -201,9 +201,9 @@ TestResult test_infer_request_inference(const std::string& model, const std::str
 }
 
 TestResult test_reinfer_request_inference(InferenceEngine::InferRequest& infer_request,
-                                          InferenceEngine::CNNNetwork& cnnNetwork, const std::string& model,
+                                          InferenceEngine::OutputsDataMap& output_info, const std::string& model,
                                           const std::string& target_device, const int& n) {
     log_info("Inference of InferRequest from network: \"" << model << "\" for device: \"" << target_device << "\" for "
                                                           << n << " times");
-    return common_test_pipeline(reinfer_request_inference(infer_request, cnnNetwork), n);
+    return common_test_pipeline(reinfer_request_inference(infer_request, output_info), n);
 }

diff --git a/tests/stress_tests/memleaks_tests/tests_pipelines/tests_pipelines.h b/tests/stress_tests/memleaks_tests/tests_pipelines/tests_pipelines.h
index 79ac564..37a891b 100644 (file)
--- a/tests/stress_tests/memleaks_tests/tests_pipelines/tests_pipelines.h
+++ b/tests/stress_tests/memleaks_tests/tests_pipelines/tests_pipelines.h
@@ -21,5 +21,5 @@ TestResult test_recreate_exenetwork(InferenceEngine::Core &ie, const std::string
 TestResult test_create_infer_request(const std::string &model, const std::string &target_device, const int &n);
 TestResult test_recreate_infer_request(InferenceEngine::ExecutableNetwork& network, const std::string &model, const std::string &target_device, const int &n);
 TestResult test_infer_request_inference(const std::string &model, const std::string &target_device, const int &n);
-TestResult test_reinfer_request_inference(InferenceEngine::InferRequest& infer_request, InferenceEngine::CNNNetwork& cnnNetwork, const std::string &model, const std::string &target_device, const int &n);
+TestResult test_reinfer_request_inference(InferenceEngine::InferRequest& infer_request, InferenceEngine::OutputsDataMap& output_info, const std::string &model, const std::string &target_device, const int &n);
 // tests_pipelines/tests_pipelines.cpp

diff --git a/tests/stress_tests/unittests/tests_pipelines/tests_pipelines_full_pipeline.cpp b/tests/stress_tests/unittests/tests_pipelines/tests_pipelines_full_pipeline.cpp
index 51a9e3c..d3764d2 100644 (file)
--- a/tests/stress_tests/unittests/tests_pipelines/tests_pipelines_full_pipeline.cpp
+++ b/tests/stress_tests/unittests/tests_pipelines/tests_pipelines_full_pipeline.cpp
@@ -3,6 +3,7 @@
 //
 
 #include "tests_pipelines.h"
+#include "../common/ie_utils.h"
 
 #include <string>
 
@@ -56,6 +57,12 @@ void test_load_unload_plugin_full_pipeline(const std::string &model, const std::
     reshapeCNNNetwork();
     ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, target_device);
     InferRequest infer_request = exeNetwork.CreateInferRequest();
+
+    auto batchSize = cnnNetwork.getBatchSize();
+    batchSize = batchSize != 0 ? batchSize : 1;
+    const ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+    fillBlobs(infer_request, inputsInfo, batchSize);
+
     infer_request.Infer();
     OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
     for (auto &output : output_info)
@@ -82,6 +89,12 @@ void test_read_network_full_pipeline(const std::string &model, const std::string
     reshapeCNNNetwork();
     ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, target_device);
     InferRequest infer_request = exeNetwork.CreateInferRequest();
+
+    auto batchSize = cnnNetwork.getBatchSize();
+    batchSize = batchSize != 0 ? batchSize : 1;
+    const ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+    fillBlobs(infer_request, inputsInfo, batchSize);
+
     infer_request.Infer();
     OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
     for (auto &output : output_info)
@@ -109,6 +122,12 @@ void test_set_input_params_full_pipeline(const std::string &model, const std::st
     reshapeCNNNetwork();
     ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, target_device);
     InferRequest infer_request = exeNetwork.CreateInferRequest();
+
+    auto batchSize = cnnNetwork.getBatchSize();
+    batchSize = batchSize != 0 ? batchSize : 1;
+    const ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+    fillBlobs(infer_request, inputsInfo, batchSize);
+
     infer_request.Infer();
     OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
     for (auto &output : output_info)
@@ -151,6 +170,12 @@ void test_cnnnetwork_reshape_batch_x2_full_pipeline(const std::string &model, co
     }
     ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, target_device);
     InferRequest infer_request = exeNetwork.CreateInferRequest();
+
+    auto batchSize = cnnNetwork.getBatchSize();
+    batchSize = batchSize != 0 ? batchSize : 1;
+    const ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+    fillBlobs(infer_request, inputsInfo, batchSize);
+
     infer_request.Infer();
     OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
     for (auto &output : output_info)
@@ -178,6 +203,12 @@ void test_create_exenetwork_full_pipeline(const std::string &model, const std::s
         exeNetwork = ie.LoadNetwork(cnnNetwork, target_device);
     }
     InferRequest infer_request = exeNetwork.CreateInferRequest();
+
+    auto batchSize = cnnNetwork.getBatchSize();
+    batchSize = batchSize != 0 ? batchSize : 1;
+    const ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+    fillBlobs(infer_request, inputsInfo, batchSize);
+
     infer_request.Infer();
     OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
     for (auto &output : output_info)
@@ -199,11 +230,16 @@ void test_create_infer_request_full_pipeline(const std::string &model, const std
     reshapeCNNNetwork();
     ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, target_device);
     InferRequest infer_request;
+
+    auto batchSize = cnnNetwork.getBatchSize();
+    batchSize = batchSize != 0 ? batchSize : 1;
+    const ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
     for (int i = 0; i < n; i++) {
         if (i == n / 2) {
             log_info("Half of the test have already passed");
         }
         infer_request = exeNetwork.CreateInferRequest();
+        fillBlobs(infer_request, inputsInfo, batchSize);
     }
     infer_request.Infer();
     OutputsDataMap output_info(cnnNetwork.getOutputsInfo());
@@ -226,6 +262,12 @@ void test_infer_request_inference_full_pipeline(const std::string &model, const
     reshapeCNNNetwork();
     ExecutableNetwork exeNetwork = ie.LoadNetwork(cnnNetwork, target_device);
     InferRequest infer_request = exeNetwork.CreateInferRequest();
+
+    auto batchSize = cnnNetwork.getBatchSize();
+    batchSize = batchSize != 0 ? batchSize : 1;
+    const ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+    fillBlobs(infer_request, inputsInfo, batchSize);
+
     for (int i = 0; i < n; i++) {
         if (i == n / 2) {
             log_info("Half of the test have already passed");