Release 18.03
diff --git a/src/armnnTfParser/test/Convolution2d.cpp b/src/armnnTfParser/test/Convolution2d.cpp
new file mode 100644
index 0000000..a7c7648
--- /dev/null
+++ b/src/armnnTfParser/test/Convolution2d.cpp
@@ -0,0 +1,322 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+
+#include <boost/test/unit_test.hpp>
+#include "armnnTfParser/ITfParser.hpp"
+#include "ParserPrototxtFixture.hpp"
+#include <string>
+#include <iostream>
+
+BOOST_AUTO_TEST_SUITE(TensorflowParser)
+
+struct Convolution2dFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+{
+ explicit Convolution2dFixture(const char* paddingType)
+ : Convolution2dFixture(paddingType, 1)
+ {}
+
+ // dilation: 0 - dilations attribute is not included;
+ // dilation: >0 - dilations attribute set to [1,v,v,1], where v is the value of the dilation arg
+ explicit Convolution2dFixture(const char* paddingType, int stride, int dilation = 0)
+ {
+ std::string strideString = std::to_string(stride);
+ std::string dilationString = std::to_string(dilation);
+ m_Prototext = "node { \n"
+ " name: \"graphInput\" \n"
+ " op: \"Placeholder\" \n"
+ " attr { \n"
+ " key: \"dtype\" \n"
+ " value { \n"
+ " type: DT_FLOAT \n"
+ " } \n"
+ " } \n"
+ " attr { \n"
+ " key: \"shape\" \n"
+ " value { \n"
+ " shape { \n"
+ " } \n"
+ " } \n"
+ " } \n"
+ " } \n"
+ " node { \n"
+ " name: \"Const_1\" \n"
+ " op: \"Const\" \n"
+ " attr { \n"
+ " key: \"dtype\" \n"
+ " value { \n"
+ " type: DT_FLOAT \n"
+ " } \n"
+ " } \n"
+ " attr { \n"
+ " key: \"value\" \n"
+ " value { \n"
+ " tensor { \n"
+ " dtype: DT_FLOAT \n"
+ " tensor_shape { \n"
+ " dim { \n"
+ " size: 1 \n"
+ " } \n"
+ " dim { \n"
+ " size: 3 \n"
+ " } \n"
+ " dim { \n"
+ " size: 1 \n"
+ " } \n"
+ " dim { \n"
+ " size: 1 \n"
+ " } \n"
+ " } \n"
+ " tensor_content: \"\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?\" \n"
+ " } \n"
+ " } \n"
+ " } \n"
+ "} \n"
+ "node { \n"
+ " name: \"potato\" \n"
+ " op: \"Conv2D\" \n"
+ " input: \"graphInput\" \n"
+ " input: \"Const_1\" \n"
+ " attr { \n"
+ " key: \"T\" \n"
+ " value { \n"
+ " type: DT_FLOAT \n"
+ " } \n"
+ " } \n"
+ " attr { \n"
+ " key: \"data_format\" \n"
+ " value { \n"
+ " s: \"NHWC\" \n"
+ " } \n"
+ " } \n"
+ " attr { \n"
+ " key: \"padding\" \n"
+ " value { \n"
+ " s: \"";
+ m_Prototext.append(paddingType);
+ m_Prototext.append("\"\n"
+ " } \n"
+ " } \n"
+ " attr { \n"
+ " key: \"strides\" \n"
+ " value { \n"
+ " list { \n"
+ " i: 1 \n"
+ " i: 1 \n"
+ " i: ");
+ m_Prototext.append(strideString);
+ m_Prototext.append(" \n"
+ " i: 1 \n"
+ " } \n"
+ " } \n"
+ " } \n");
+
+ if (dilation > 0)
+ {
+ m_Prototext.append(" attr { \n"
+ " key: \"dilations\" \n"
+ " value { \n"
+ " list { \n"
+ " i: 1 \n"
+ " i: ");
+ m_Prototext.append(dilationString);
+ m_Prototext.append(" \n"
+ " i: ");
+ m_Prototext.append(dilationString);
+ m_Prototext.append(" \n"
+ " i: 1 \n"
+ " } \n"
+ " } \n"
+ " } \n");
+ }
+ m_Prototext.append(" attr { \n"
+ " key: \"use_cudnn_on_gpu\" \n"
+ " value { \n"
+ " b: false \n"
+ " } \n"
+ " } \n"
+ "} \n");
+
+ // Manual height computation based on stride parameter.
+ BOOST_ASSERT_MSG(stride == 1 || stride==2, "Add support for strides other than 1 or 2.");
+ unsigned int dims[] = {1,2,3,1};
+ if (stride == 2)
+ {
+ dims[1]=3;
+ }
+
+ SetupSingleInputSingleOutput(armnn::TensorShape(4, dims), "graphInput", "potato");
+ }
+};
+
+
+struct Convolution2dSameFixture : Convolution2dFixture
+{
+ Convolution2dSameFixture() : Convolution2dFixture("SAME", 1){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2DSame, Convolution2dSameFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6}, {2, 4, 4, 6.5f, 10 , 8.5f});
+}
+
+struct Convolution2dValidFixture : Convolution2dFixture
+{
+ Convolution2dValidFixture() : Convolution2dFixture("VALID", 1){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2DValid, Convolution2dValidFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6}, {4, 10});
+}
+
+
+struct Convolution2dStride2SameFixture : Convolution2dFixture
+{
+ Convolution2dStride2SameFixture() : Convolution2dFixture("SAME", 2){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2DStride2Same, Convolution2dStride2SameFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {2, 4, 6.5, 8.5, 11, 13});
+}
+
+
+struct Convolution2dStride2ValidFixture : Convolution2dFixture
+{
+ Convolution2dStride2ValidFixture() : Convolution2dFixture("VALID", 2){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2DStride2Valid, Convolution2dStride2ValidFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {4, 10, 16});
+}
+
+
+struct Convolution2dDilation1Fixture : Convolution2dFixture
+{
+ Convolution2dDilation1Fixture() : Convolution2dFixture("SAME", 1, 1){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2DDilation1, Convolution2dDilation1Fixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6}, {2, 4, 4, 6.5f, 10 , 8.5f});
+}
+
+BOOST_AUTO_TEST_CASE(ParseConv2DDilation2)
+{
+ const char* prototext = ""
+ "node {\n"
+ " name: \"graphInput\"\n"
+ " op: \"Placeholder\"\n"
+ " attr {\n"
+ " key: \"dtype\"\n"
+ " value {\n"
+ " type: DT_FLOAT\n"
+ " }\n"
+ " }\n"
+ " attr {\n"
+ " key: \"shape\"\n"
+ " value {\n"
+ " shape {\n"
+ " }\n"
+ " }\n"
+ " }\n"
+ "}\n"
+ "node {\n"
+ " name: \"Const_1\"\n"
+ " op: \"Const\"\n"
+ " attr {\n"
+ " key: \"dtype\"\n"
+ " value {\n"
+ " type: DT_FLOAT\n"
+ " }\n"
+ " }\n"
+ " attr {\n"
+ " key: \"value\"\n"
+ " value {\n"
+ " tensor {\n"
+ " dtype: DT_FLOAT\n"
+ " tensor_shape {\n"
+ " dim {\n"
+ " size: 1\n"
+ " }\n"
+ " dim {\n"
+ " size: 3\n"
+ " }\n"
+ " dim {\n"
+ " size: 1\n"
+ " }\n"
+ " dim {\n"
+ " size: 1\n"
+ " }\n"
+ " }\n"
+ " tensor_content: \"\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?\"\n"
+ " }\n"
+ " }\n"
+ " }\n"
+ "}\n"
+ "node {\n"
+ " name: \"potato\"\n"
+ " op: \"Conv2D\"\n"
+ " input: \"graphInput\"\n"
+ " input: \"Const_1\"\n"
+ " attr {\n"
+ " key: \"T\"\n"
+ " value {\n"
+ " type: DT_FLOAT\n"
+ " }\n"
+ " }\n"
+ " attr {\n"
+ " key: \"data_format\"\n"
+ " value {\n"
+ " s: \"NHWC\"\n"
+ " }\n"
+ " }\n"
+ " attr {\n"
+ " key: \"padding\"\n"
+ " value {\n"
+ " s: \"SAME\"\n"
+ " }\n"
+ " }\n"
+ " attr {\n"
+ " key: \"strides\"\n"
+ " value {\n"
+ " list {\n"
+ " i: 1\n"
+ " i: 1\n"
+ " i: 1\n"
+ " i: 1\n"
+ " }\n"
+ " }\n"
+ " }\n"
+ " attr {\n"
+ " key: \"dilations\"\n"
+ " value {\n"
+ " list {\n"
+ " i: 1\n"
+ " i: 2\n"
+ " i: 2\n"
+ " i: 1\n"
+ " }\n"
+ " }\n"
+ " }\n"
+ " attr {\n"
+ " key: \"use_cudnn_on_gpu\"\n"
+ " value {\n"
+ " b: false\n"
+ " }\n"
+ " }\n"
+ "}\n";
+
+ std::map<std::string, armnn::TensorShape> inputShapes;
+ armnn::TensorShape tensorShape = { 1, 3, 3, 1 };
+ inputShapes["graphInput"] = tensorShape;
+ armnnTfParser::ITfParserPtr parser = armnnTfParser::ITfParser::Create();
+ BOOST_CHECK_EXCEPTION(parser->CreateNetworkFromString(prototext, inputShapes, { "potato" }),
+ armnn::ParseException,
+ [] (armnn::ParseException const& ex)->bool
+ {
+ return strcmp(ex.what(),
+ "ArmNN only supports Convolution layers with dilations [1,1,1,1]") == 0;
+ });
+}
+
+
+BOOST_AUTO_TEST_SUITE_END()