1 files changed, 145 insertions, 0 deletions
diff --git a/tests/nnfw_api/src/one_op_tests/Mul.test.cc b/tests/nnfw_api/src/one_op_tests/Mul.test.cc
new file mode 100644
index 000000000..0c7944613
--- /dev/null
+++ b/tests/nnfw_api/src/one_op_tests/Mul.test.cc
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2020 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 "GenModelTest.h"
+
+#include <memory>
+
+TEST_F(GenModelTest, OneOp_Mul_Uint8_VarVar)
+{
+  CircleGen cgen;
+  int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_UINT8}, 1.0, 3);
+  int rhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_UINT8}, 2.0, 1);
+  int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_UINT8}, 0.5, 2);
+  cgen.addOperatorMul({{lhs, rhs}, {out}}, circle::ActivationFunctionType_NONE);
+  cgen.setInputsAndOutputs({lhs, rhs}, {out});
+
+  _context = std::make_unique<GenModelTestContext>(cgen.finish());
+  _context->addTestCase(uniformTCD<uint8_t>({{3, 12, 5, 2}, {5, 4, 7, 0}}, {{2, 110, 50, 6}}));
+  _context->setBackends({"acl_cl", "acl_neon", "cpu"});
+
+  SUCCEED();
+}
+
+TEST_F(GenModelTest, OneOp_Mul_Int8_VarVar)
+{
+  CircleGen cgen;
+  int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT8}, 1.0, 2);
+  int rhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT8}, 2.0, 3);
+  int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT8}, 0.5, -6);
+  cgen.addOperatorMul({{lhs, rhs}, {out}}, circle::ActivationFunctionType_NONE);
+  cgen.setInputsAndOutputs({lhs, rhs}, {out});
+
+  _context = std::make_unique<GenModelTestContext>(cgen.finish());
+  _context->addTestCase(uniformTCD<int8_t>({{1, 3, 2, 4}, {5, -4, -7, 4}}, {{-14, -34, -6, 2}}));
+  _context->setBackends({"acl_cl", "acl_neon", "cpu"});
+
+  SUCCEED();
+}
+
+TEST_F(GenModelTest, OneOp_MulBroadcast_Uint8_VarVar)
+{
+  CircleGen cgen;
+  int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_UINT8}, 1.0, 3);
+  int rhs = cgen.addTensor({{1, 1, 1, 1}, circle::TensorType::TensorType_UINT8}, 2.0, 1);
+  int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_UINT8}, 0.5, 2);
+  cgen.addOperatorMul({{lhs, rhs}, {out}}, circle::ActivationFunctionType_NONE);
+  cgen.setInputsAndOutputs({lhs, rhs}, {out});
+
+  _context = std::make_unique<GenModelTestContext>(cgen.finish());
+  _context->addTestCase(uniformTCD<uint8_t>({{3, 12, 5, 4}, {5}}, {{2, 146, 34, 18}}));
+  _context->setBackends({"acl_cl", "acl_neon", "cpu"});
+
+  SUCCEED();
+}
+
+TEST_F(GenModelTest, OneOp_MulBroadcast_Int8_VarVar)
+{
+  CircleGen cgen;
+  int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT8}, 1.0, 2);
+  int rhs = cgen.addTensor({{1, 1, 1, 1}, circle::TensorType::TensorType_INT8}, 2.0, 3);
+  int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT8}, 0.5, -6);
+  cgen.addOperatorMul({{lhs, rhs}, {out}}, circle::ActivationFunctionType_NONE);
+  cgen.setInputsAndOutputs({lhs, rhs}, {out});
+
+  _context = std::make_unique<GenModelTestContext>(cgen.finish());
+  _context->addTestCase(uniformTCD<int8_t>({{1, 3, 2, 4}, {5}}, {{-14, 2, -6, 10}}));
+  _context->setBackends({"acl_cl", "acl_neon", "cpu"});
+
+  SUCCEED();
+}
+
+TEST_F(GenModelTest, neg_OneOp_Mul_InvalidType)
+{
+  CircleGen cgen;
+  int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
+  int rhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_UINT8}, 0.1, 2);
+  int out = cgen.addTensor({{1, 2, 3, 1}, circle::TensorType::TensorType_FLOAT32});
+  cgen.addOperatorMul({{lhs, rhs}, {out}}, circle::ActivationFunctionType_NONE);
+  cgen.setInputsAndOutputs({lhs, rhs}, {out});
+
+  _context = std::make_unique<GenModelTestContext>(cgen.finish());
+  _context->setBackends({"acl_cl", "acl_neon", "cpu"});
+  _context->expectFailModelLoad();
+
+  SUCCEED();
+}
+
+TEST_F(GenModelTest, neg_OneOp_Mul_InvalidShape)
+{
+  CircleGen cgen;
+  int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
+  int rhs = cgen.addTensor({{1, 2, 3, 1}, circle::TensorType::TensorType_FLOAT32});
+  int out = cgen.addTensor({{1, 2, 3, 1}, circle::TensorType::TensorType_FLOAT32});
+  cgen.addOperatorMul({{lhs, rhs}, {out}}, circle::ActivationFunctionType_NONE);
+  cgen.setInputsAndOutputs({lhs, rhs}, {out});
+
+  _context = std::make_unique<GenModelTestContext>(cgen.finish());
+  _context->setBackends({"acl_cl", "acl_neon", "cpu"});
+  _context->expectFailCompile();
+
+  SUCCEED();
+}
+
+TEST_F(GenModelTest, neg_OneOp_Mul_OneOperand)
+{
+  CircleGen cgen;
+  int in = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
+  int out = cgen.addTensor({{1, 2, 3, 1}, circle::TensorType::TensorType_FLOAT32});
+  cgen.addOperatorMul({{in}, {out}}, circle::ActivationFunctionType_NONE);
+  cgen.setInputsAndOutputs({in}, {out});
+
+  _context = std::make_unique<GenModelTestContext>(cgen.finish());
+  _context->setBackends({"acl_cl", "acl_neon", "cpu"});
+  _context->expectFailModelLoad();
+
+  SUCCEED();
+}
+
+TEST_F(GenModelTest, neg_OneOp_Mul_ThreeOperands)
+{
+  CircleGen cgen;
+  int in = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
+  int out = cgen.addTensor({{1, 2, 3, 1}, circle::TensorType::TensorType_FLOAT32});
+  cgen.addOperatorMul({{in, in, in}, {out}}, circle::ActivationFunctionType_NONE);
+  cgen.setInputsAndOutputs({in}, {out});
+
+  _context = std::make_unique<GenModelTestContext>(cgen.finish());
+  _context->setBackends({"acl_cl", "acl_neon", "cpu"});
+  _context->expectFailModelLoad();
+
+  SUCCEED();
+}