IVGCVSW-2029 Fix fully connected layer support in TfLite Parser and implement test...

Change-Id: I2061f62f62684b963fa0f090718f1dcffe5c93ce

diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index 8b1d3e6..5e0d4b7 100644 (file)
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -1231,6 +1231,7 @@ void TfLiteParser::ParseFullyConnected(size_t subgraphIndex, size_t operatorInde
 
     FullyConnectedDescriptor desc;
     desc.m_BiasEnabled = false;
+    desc.m_TransposeWeightMatrix = true;
 
     auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex);
     auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex);

diff --git a/src/armnnTfLiteParser/test/FullyConnected.cpp b/src/armnnTfLiteParser/test/FullyConnected.cpp
index 2853fe9..14ca57c 100644 (file)
--- a/src/armnnTfLiteParser/test/FullyConnected.cpp
+++ b/src/armnnTfLiteParser/test/FullyConnected.cpp
@@ -118,7 +118,7 @@ struct FullyConnectedWithNoBiasFixture : FullyConnectedFixture
     FullyConnectedWithNoBiasFixture()
         : FullyConnectedFixture("[ 1, 4, 1, 1 ]",     // inputShape
                                 "[ 1, 1 ]",           // outputShape
-                                "[ 4, 1 ]",           // filterShape
+                                "[ 1, 4 ]",           // filterShape
                                 "[ 2, 3, 4, 5 ]")     // filterData
     {}
 };
@@ -136,7 +136,7 @@ struct FullyConnectedWithBiasFixture : FullyConnectedFixture
     FullyConnectedWithBiasFixture()
         : FullyConnectedFixture("[ 1, 4, 1, 1 ]",     // inputShape
                                 "[ 1, 1 ]",           // outputShape
-                                "[ 4, 1 ]",           // filterShape
+                                "[ 1, 4 ]",           // filterShape
                                 "[ 2, 3, 4, 5 ]",     // filterData
                                 "[ 1 ]",              // biasShape
                                 "[ 10, 0, 0, 0 ]" )   // biasData

diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 97f2115..d6475c2 100644 (file)
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -163,6 +163,12 @@ if (BUILD_TF_LITE_PARSER)
         ImagePreprocessor.hpp
         ImagePreprocessor.cpp)
     TfLiteParserTest(TfLiteMobilenetQuantized-Armnn "${TfLiteMobilenetQuantized-Armnn_sources}")
+
+    set(TfLiteVGG16Quantized-Armnn_sources
+        TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
+        ImagePreprocessor.hpp
+        ImagePreprocessor.cpp)
+    TfLiteParserTest(TfLiteVGG16Quantized-Armnn "${TfLiteVGG16Quantized-Armnn_sources}")
 endif()
 
 if (BUILD_ONNX_PARSER)

diff --git a/tests/ImagePreprocessor.cpp b/tests/ImagePreprocessor.cpp
index 1f29cff..8ceedd2 100644 (file)
--- a/tests/ImagePreprocessor.cpp
+++ b/tests/ImagePreprocessor.cpp
@@ -33,10 +33,16 @@ unsigned int ImagePreprocessor<TDataType>::GetLabelAndResizedImageAsFloat(unsign
                           InferenceTestImage::ResizingMethods::BilinearAndNormalized,
                           m_Mean, m_Stddev);
 
+    // duplicate data across the batch
+    for (unsigned int i = 1; i < m_BatchSize; i++)
+    {
+        result.insert( result.end(), result.begin(), result.begin() + GetNumImageElements() );
+    }
+
     if (m_DataFormat == DataFormat::NCHW)
     {
         const armnn::PermutationVector NHWCToArmNN = { 0, 2, 3, 1 };
-        armnn::TensorShape dstShape({1, 3, m_Height, m_Width});
+        armnn::TensorShape dstShape({m_BatchSize, 3, m_Height, m_Width});
         std::vector<float> tempImage(result.size());
         armnnUtils::Permute<float>(dstShape, NHWCToArmNN, result.data(), tempImage.data());
         result.swap(tempImage);

diff --git a/tests/ImagePreprocessor.hpp b/tests/ImagePreprocessor.hpp
index 9add6d8..d77113c 100644 (file)
--- a/tests/ImagePreprocessor.hpp
+++ b/tests/ImagePreprocessor.hpp
@@ -37,10 +37,12 @@ public:
         int32_t offset=0,
         const std::array<float, 3> mean={{0, 0, 0}},
         const std::array<float, 3> stddev={{1, 1, 1}},
-        DataFormat dataFormat=DataFormat::NHWC)
+        DataFormat dataFormat=DataFormat::NHWC,
+        unsigned int batchSize=1)
     : m_BinaryDirectory(binaryFileDirectory)
     , m_Height(height)
     , m_Width(width)
+    , m_BatchSize(batchSize)
     , m_Scale(scale)
     , m_Offset(offset)
     , m_ImageSet(imageSet)
@@ -61,6 +63,7 @@ private:
     std::string m_BinaryDirectory;
     unsigned int m_Height;
     unsigned int m_Width;
+    unsigned int m_BatchSize;
     // Quantization parameters
     float m_Scale;
     int32_t m_Offset;

diff --git a/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp b/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
new file mode 100644 (file)
index 0000000..1313d2d
--- /dev/null
+++ b/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
@@ -0,0 +1,68 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include "../InferenceTest.hpp"
+#include "../ImagePreprocessor.hpp"
+#include "armnnTfLiteParser/ITfLiteParser.hpp"
+
+using namespace armnnTfLiteParser;
+
+int main(int argc, char* argv[])
+{
+    int retVal = EXIT_FAILURE;
+    try
+    {
+        // Coverity fix: The following code may throw an exception of type std::length_error.
+        std::vector<ImageSet> imageSet =
+        {
+            // Class number in probability print out offset by 1000 due to batch size fix
+            {"Dog.jpg", 669},
+            {"Cat.jpg", 669},
+            {"shark.jpg", 669},
+        };
+
+        armnn::TensorShape inputTensorShape({ 2, 224, 224, 3 });
+
+        using DataType = uint8_t;
+        using DatabaseType = ImagePreprocessor<DataType>;
+        using ParserType = armnnTfLiteParser::ITfLiteParser;
+        using ModelType = InferenceModel<ParserType, DataType>;
+
+        // Coverity fix: ClassifierInferenceTestMain() may throw uncaught exceptions.
+        retVal = armnn::test::ClassifierInferenceTestMain<DatabaseType,
+                                                          ParserType>(
+                     argc, argv,
+                     "vgg_16_u8.tflite",                  // model name
+                     true,                                // model is binary
+                     "content_vgg/concat",                // input tensor name
+                     "content_vgg/prob",                  // output tensor name
+                     { 0, 1, 2 },                         // test images to test with as above
+                     [&imageSet](const char* dataDir, const ModelType & model) {
+                         // we need to get the input quantization parameters from
+                         // the parsed model
+                         auto inputBinding = model.GetInputBindingInfo();
+                         return DatabaseType(
+                             dataDir,
+                             224,
+                             224,
+                             imageSet,
+                             inputBinding.second.GetQuantizationScale(),
+                             inputBinding.second.GetQuantizationOffset(),
+                             {{0, 0, 0}},
+                             {{1, 1, 1}},
+                             DatabaseType::DataFormat::NCHW,
+                             2);
+                     },
+                     &inputTensorShape);
+    }
+    catch (const std::exception& e)
+    {
+        // Coverity fix: BOOST_LOG_TRIVIAL (typically used to report errors) may throw an
+        // exception of type std::length_error.
+        // Using stderr instead in this context as there is no point in nesting try-catch blocks here.
+        std::cerr << "WARNING: " << *argv << ": An error has occurred when running "
+                     "the classifier inference tests: " << e.what() << std::endl;
+    }
+    return retVal;
+}