Release 18.03
diff --git a/src/armnnTfParser/test/DepthwiseConvolution2d.cpp b/src/armnnTfParser/test/DepthwiseConvolution2d.cpp
new file mode 100644
index 0000000..84e7a7e
--- /dev/null
+++ b/src/armnnTfParser/test/DepthwiseConvolution2d.cpp
@@ -0,0 +1,166 @@
+//
+// 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 DepthwiseConvolution2dFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+{
+ explicit DepthwiseConvolution2dFixture(const char* paddingType)
+ {
+ 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: \"value\" \n"
+ " value { \n"
+ " tensor { \n"
+ " dtype: DT_FLOAT \n"
+ " tensor_shape { \n"
+ " dim { \n"
+ " size: 1 \n"
+ " } \n"
+ " dim { \n"
+ " size: 1 \n"
+ " } \n"
+ " dim { \n"
+ " size: 3 \n"
+ " } \n"
+ " dim { \n"
+ " size: 3 \n"
+ " } \n"
+ " } \n"
+ " tensor_content: \"\\000\\000\\200?\\000\\000\\000@\\000\\000@@\\000\\000\\200@"
+ "\\000\\000\\240@\\000\\000\\300@\\000\\000\\340@\\000\\000\\000A\\000\\000\\020A\" \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: 3 \n"
+ " } \n"
+ " dim { \n"
+ " size: 3 \n"
+ " } \n"
+ " } \n"
+ " tensor_content: \"\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?"
+ "\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?"
+ "\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?"
+ "\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?"
+ "\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?"
+ "\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?"
+ "\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?"
+ "\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?"
+ "\\000\\000\\000?\\000\\000\\200?\\000\\000\\000?\" \n"
+ " } \n"
+ " } \n"
+ " } \n"
+ "} \n"
+ "node { \n"
+ " name: \"potato\" \n"
+ " op: \"DepthwiseConv2dNative\" \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: 1 \n"
+ " i: 1 \n"
+ " } \n"
+ " } \n"
+ " } \n"
+ " attr { \n"
+ " key: \"use_cudnn_on_gpu\" \n"
+ " value { \n"
+ " b: false \n"
+ " } \n"
+ " } \n"
+ "} \n");
+
+ SetupSingleInputSingleOutput({ 1, 1, 3, 3 }, "graphInput", "potato");
+ }
+};
+
+struct DepthwiseConvolution2dSameFixture : DepthwiseConvolution2dFixture
+{
+ DepthwiseConvolution2dSameFixture() : DepthwiseConvolution2dFixture("SAME") { }
+};
+
+BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DSame, DepthwiseConvolution2dSameFixture)
+{
+ RunTest<4>({ 1, 2, 3, 4, 5, 6, 7, 8, 9 },
+ { 2.5f, 5.f, 2.5f, 3.5f, 7.f, 3.5f, 4.5f, 9.f, 4.5f,
+ 6.f, 12.f, 6.f, 7.5f, 15.f, 7.5f, 9.f, 18.f, 9.f,
+ 5.5f, 11.f, 5.5f, 6.5f, 13.f, 6.5f, 7.5f, 15.f, 7.5f});
+}
+
+struct DepthwiseConvolution2dValidFixture : DepthwiseConvolution2dFixture
+{
+ DepthwiseConvolution2dValidFixture() : DepthwiseConvolution2dFixture("VALID") { }
+};
+
+BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DValid, DepthwiseConvolution2dValidFixture)
+{
+ RunTest<4>({ 1, 2, 3, 4, 5, 6, 7, 8, 9 }, // input data
+ { 6.f, 12.f, 6.f, 7.5f, 15.f, 7.5f, 9.f, 18.f, 9.f }); // output expected data
+}
+
+
+BOOST_AUTO_TEST_SUITE_END()