1 files changed, 70 insertions, 0 deletions
diff --git a/compiler/nnc/unittests/optimizations/FuseArithmeticOps.cpp b/compiler/nnc/unittests/optimizations/FuseArithmeticOps.cpp
new file mode 100644
index 000000000..85a2fee76
--- /dev/null
+++ b/compiler/nnc/unittests/optimizations/FuseArithmeticOps.cpp
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "passes/optimizations/FuseArithmeticOps.h"
+#include "Util.h"
+#include "mir/Graph.h"
+#include "mir/ops/AddOp.h"
+#include "mir/ops/ConstantOp.h"
+#include "mir/ops/Conv2DOp.h"
+#include "mir/ops/MulOp.h"
+
+#include <gtest/gtest.h>
+#include <sstream>
+
+using namespace nnc;
+using namespace mir;
+
+namespace
+{
+
+TEST(OptPass, fuseConvBiasScaleScaleBias)
+{
+  mir::Graph g;
+
+  // Create graph: 'input->conv->bias->scale->scale->bias'
+  mir::TensorType input_type(mir::DataType::FLOAT32, Shape{1, 299, 299, 3});
+  auto input = g.create<ops::InputOp>(input_type);
+  auto conv_const = g.create<ops::ConstantOp>(TensorVariant(DataType::FLOAT32, {10, 3, 3, 3}));
+  auto conv = g.create<ops::Conv2DOp>(input->getOutput(0), conv_const->getOutput(0),
+                                      mir::Conv2DOpAttributes());
+  auto bias1_const = g.create<ops::ConstantOp>(TensorVariant(DataType::FLOAT32, {10}));
+  auto bias1 = g.create<ops::AddOp>(conv->getOutput(0), bias1_const->getOutput(0));
+  auto scale1_const = g.create<ops::ConstantOp>(TensorVariant(DataType::FLOAT32, {10}));
+  auto scale1 = g.create<ops::MulOp>(bias1->getOutput(0), scale1_const->getOutput(0));
+  auto scale2_const = g.create<ops::ConstantOp>(TensorVariant(DataType::FLOAT32, {10}));
+  auto scale2 = g.create<ops::MulOp>(scale1->getOutput(0), scale2_const->getOutput(0));
+  auto scale3_const = g.create<ops::ConstantOp>(TensorVariant(DataType::FLOAT32, {10}));
+  auto scale3 = g.create<ops::MulOp>(scale2->getOutput(0), scale3_const->getOutput(0));
+  auto bias2_const = g.create<ops::ConstantOp>(TensorVariant(DataType::FLOAT32, {10}));
+  g.create<ops::AddOp>(scale3->getOutput(0), bias2_const->getOutput(0));
+
+  // Check that layout is desired
+  std::stringstream ss;
+  DumpVisitor d(ss);
+  FuseArithmeticOps pass;
+  pass.run(&g);
+  g.accept(&d);
+  // Assert only 'conv->bias' remains
+  ASSERT_TRUE("i_0.const_25.const_23.conv_26.b_24." == ss.str() ||
+              "i_0.const_23.const_25.conv_26.b_24." == ss.str() ||
+              "const_25.i_0.const_23.conv_26.b_24." == ss.str() ||
+              "const_23.i_0.const_25.conv_26.b_24." == ss.str() ||
+              "const_25.const_23.i_0.conv_26.b_24." == ss.str() ||
+              "const_23.const_25.i_0.conv_26.b_24." == ss.str());
+}
+
+} // unnamed namespace