Release 18.08

1 files changed, 74 insertions, 0 deletions

diff --git a/tests/ImagePreprocessor.cpp b/tests/ImagePreprocessor.cpp
new file mode 100644
index 0000000000..4e46b914ae
--- /dev/null
+++ b/tests/ImagePreprocessor.cpp
@@ -0,0 +1,74 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include "InferenceTestImage.hpp"
+#include "ImagePreprocessor.hpp"
+#include "Permute.hpp"
+#include <armnn/TypesUtils.hpp>
+
+#include <boost/numeric/conversion/cast.hpp>
+#include <boost/assert.hpp>
+#include <boost/format.hpp>
+
+#include <iostream>
+#include <fcntl.h>
+#include <array>
+
+template <typename TDataType>
+unsigned int ImagePreprocessor<TDataType>::GetLabelAndResizedImageAsFloat(unsigned int testCaseId,
+                                                                          std::vector<float> & result)
+{
+    testCaseId = testCaseId % boost::numeric_cast<unsigned int>(m_ImageSet.size());
+    const ImageSet& imageSet = m_ImageSet[testCaseId];
+    const std::string fullPath = m_BinaryDirectory + imageSet.first;
+
+    InferenceTestImage image(fullPath.c_str());
+
+    // this ResizeBilinear result is closer to the tensorflow one than STB.
+    // there is still some difference though, but the inference results are
+    // similar to tensorflow for MobileNet
+
+    result = image.Resize(m_Width, m_Height, CHECK_LOCATION(),
+                          InferenceTestImage::ResizingMethods::BilinearAndNormalized,
+                          m_Mean, m_Stddev);
+
+    if (m_DataFormat == DataFormat::NCHW)
+    {
+        const armnn::PermutationVector NHWCToArmNN = { 0, 2, 3, 1 };
+        armnn::TensorShape dstShape({1, 3, m_Height, m_Width});
+        std::vector<float> tempImage(result.size());
+        armnnUtils::Permute<float>(dstShape, NHWCToArmNN, result.data(), tempImage.data());
+        result.swap(tempImage);
+    }
+
+    return imageSet.second;
+}
+
+template <>
+std::unique_ptr<ImagePreprocessor<float>::TTestCaseData>
+ImagePreprocessor<float>::GetTestCaseData(unsigned int testCaseId)
+{
+    std::vector<float> resized;
+    auto label = GetLabelAndResizedImageAsFloat(testCaseId, resized);
+    return std::make_unique<TTestCaseData>(label, std::move(resized));
+}
+
+template <>
+std::unique_ptr<ImagePreprocessor<uint8_t>::TTestCaseData>
+ImagePreprocessor<uint8_t>::GetTestCaseData(unsigned int testCaseId)
+{
+    std::vector<float> resized;
+    auto label = GetLabelAndResizedImageAsFloat(testCaseId, resized);
+
+    size_t resizedSize = resized.size();
+    std::vector<uint8_t> quantized(resized.size());
+
+    for (size_t i=0; i<resizedSize; ++i)
+    {
+        quantized[i] = armnn::Quantize<uint8_t>(resized[i],
+                                                m_Scale,
+                                                m_Offset);
+    }
+    return std::make_unique<TTestCaseData>(label, std::move(quantized));
+}