Add `fill_inputs` step in timetests (#2413)

diff --git a/tests/time_tests/src/timetests/common.h b/tests/time_tests/src/timetests/common.h
new file mode 100644 (file)
index 0000000..555d175
--- /dev/null
+++ b/tests/time_tests/src/timetests/common.h
@@ -0,0 +1,154 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+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) {
+  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/time_tests/src/timetests/timetest_infer.cpp b/tests/time_tests/src/timetests/timetest_infer.cpp
index 1ba0b24..fedb734 100644 (file)
--- a/tests/time_tests/src/timetests/timetest_infer.cpp
+++ b/tests/time_tests/src/timetests/timetest_infer.cpp
@@ -5,10 +5,12 @@
 #include <inference_engine.hpp>
 #include <iostream>
 
+#include "common.h"
 #include "timetests_helper/timer.h"
 #include "timetests_helper/utils.h"
 using namespace InferenceEngine;
 
+
 /**
  * @brief Function that contain executable pipeline which will be called from
  * main(). The function should not throw any exceptions and responsible for
@@ -17,6 +19,7 @@ using namespace InferenceEngine;
 int runPipeline(const std::string &model, const std::string &device) {
   auto pipeline = [](const std::string &model, const std::string &device) {
     Core ie;
+    CNNNetwork cnnNetwork;
     ExecutableNetwork exeNetwork;
     InferRequest inferRequest;
 
@@ -33,7 +36,6 @@ int runPipeline(const std::string &model, const std::string &device) {
           exeNetwork = ie.ImportNetwork(model, device);
         }
         else {
-          CNNNetwork cnnNetwork;
           {
             SCOPED_TIMER(read_network);
             cnnNetwork = ie.ReadNetwork(model);
@@ -50,6 +52,14 @@ int runPipeline(const std::string &model, const std::string &device) {
     {
       SCOPED_TIMER(first_inference);
       inferRequest = exeNetwork.CreateInferRequest();
+
+      {
+        SCOPED_TIMER(fill_inputs)
+        auto batchSize = cnnNetwork.getBatchSize();
+        batchSize = batchSize != 0 ? batchSize : 1;
+        const InferenceEngine::ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
+        fillBlobs(inferRequest, inputsInfo, batchSize);
+      }
       inferRequest.Infer();
     }
   };