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-rw-r--r--runtimes/neurun/test/graph/operation/SetIO.cc86
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diff --git a/runtimes/neurun/test/graph/operation/SetIO.cc b/runtimes/neurun/test/graph/operation/SetIO.cc
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+/*
+ * Copyright (c) 2018 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 <gtest/gtest.h>
+
+#include "graph/Graph.h"
+#include "nnfw/std/memory.h"
+#include "graph/operation/Conv2D.h"
+#include "graph/operation/Concat.h"
+#include "graph/operand/Index.h"
+#include "graph/operand/IndexSet.h"
+
+#include <stdexcept>
+
+using Index = neurun::graph::operand::IO::Index;
+using IndexSet = neurun::graph::operand::IndexSet;
+using GraphNodeInitParam = neurun::graph::operation::Node::InitParam;
+
+TEST(graph_operation_setIO, operation_setIO_conv)
+{
+ neurun::graph::Graph graph;
+
+ neurun::graph::operand::Shape shape{1u};
+ neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0};
+ shape.dim(0) = 3;
+
+ // Add Conv
+ std::vector<uint32_t> params;
+ for (int i = 0; i < 7; ++i)
+ {
+ params.emplace_back(graph.addOperand(shape, type).asInt());
+ }
+ uint32_t outoperand = graph.addOperand(shape, type).asInt();
+
+ using GraphNode = neurun::graph::operation::Conv2D::Implicit::Node;
+
+ auto conv = nnfw::make_unique<GraphNode>(GraphNodeInitParam{7, params.data(), 1, &outoperand});
+ ASSERT_EQ(conv->getInputs().at(Index{0}).asInt(), params[0]);
+ conv->setInputs({8, 9, 10});
+ ASSERT_NE(conv->getInputs().at(Index{0}).asInt(), params[0]);
+ ASSERT_EQ(conv->getInputs().at(Index{0}).asInt(), 8);
+}
+
+TEST(graph_operation_setIO, operation_setIO_concat)
+{
+ neurun::graph::Graph graph;
+
+ neurun::graph::operand::Shape shape{1u};
+ neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0};
+ shape.dim(0) = 3;
+
+ // Add Concat
+ std::vector<uint32_t> params;
+ for (int i = 0; i < 7; ++i)
+ {
+ params.emplace_back(graph.addOperand(shape, type).asInt());
+ }
+ uint32_t outoperand = graph.addOperand(shape, type).asInt();
+
+ using GraphNode = neurun::graph::operation::Concat::Node;
+
+ auto concat = nnfw::make_unique<GraphNode>(GraphNodeInitParam{7, params.data(), 1, &outoperand});
+
+ ASSERT_EQ(concat->getInputs().size(), 6);
+ ASSERT_EQ(concat->getInputs().at(Index{0}).asInt(), params[0]);
+
+ concat->setInputs({80, 6, 9, 11});
+ ASSERT_EQ(concat->getInputs().size(), 4);
+ ASSERT_NE(concat->getInputs().at(Index{0}).asInt(), params[0]);
+ ASSERT_EQ(concat->getInputs().at(Index{0}).asInt(), 80);
+ ASSERT_EQ(concat->getInputs().at(Index{2}).asInt(), 9);
+ ASSERT_THROW(concat->getInputs().at(Index{5}), std::out_of_range);
+}