1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
|
/*
* 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 "ir/Graph.h"
#include "ir/Index.h"
#include "ir/OperandIndexSequence.h"
#include "ir/operation/Conv2D.h"
#include "ir/operation/Concat.h"
#include <cpp14/memory.h>
#include <stdexcept>
using Index = neurun::ir::IOIndex;
using IndexSet = neurun::ir::OperandIndexSequence;
TEST(graph_operation_setIO, operation_setIO_conv)
{
neurun::ir::Graph graph;
neurun::ir::Shape shape{3};
neurun::ir::TypeInfo type{neurun::ir::DataType::INT32};
// Add Conv
using Graph = neurun::ir::operation::Conv2D;
auto input_operand = graph.addOperand(shape, type);
auto kernel_operand = graph.addOperand(shape, type);
auto bias_operand = graph.addOperand(shape, type);
IndexSet inputs{input_operand, kernel_operand, bias_operand};
Graph::Param conv_params;
conv_params.padding.type = neurun::ir::PaddingType::SAME;
conv_params.stride.horizontal = 1;
conv_params.stride.vertical = 1;
conv_params.activation = neurun::ir::Activation::NONE;
auto output_operand = graph.addOperand(shape, type).value();
IndexSet outputs{output_operand};
auto conv = nnfw::cpp14::make_unique<Graph>(inputs, outputs, conv_params);
ASSERT_NE(conv, nullptr);
ASSERT_EQ(conv->getInputs().at(Index{0}).value(), inputs.at(0).value());
conv->setInputs({8, 9, 10});
ASSERT_NE(conv->getInputs().at(Index{0}).value(), inputs.at(0).value());
ASSERT_EQ(conv->getInputs().at(Index{0}).value(), 8);
}
TEST(graph_operation_setIO, operation_setIO_concat)
{
neurun::ir::Graph graph;
neurun::ir::Shape shape{3};
neurun::ir::TypeInfo type{neurun::ir::DataType::INT32};
using Graph = neurun::ir::operation::Concat;
// Add Concat
IndexSet inputs;
for (int i = 0; i < 6; ++i)
{
inputs.append(graph.addOperand(shape, type));
}
Graph::Param concat_params{0};
auto output_operand = graph.addOperand(shape, type).value();
IndexSet outputs{output_operand};
auto concat = nnfw::cpp14::make_unique<Graph>(inputs, outputs, concat_params);
ASSERT_NE(concat, nullptr);
ASSERT_EQ(concat->getInputs().size(), 6);
ASSERT_EQ(concat->getInputs().at(Index{0}).value(), inputs.at(0).value());
concat->setInputs({80, 6, 9, 11});
ASSERT_EQ(concat->getInputs().size(), 4);
ASSERT_NE(concat->getInputs().at(Index{0}).value(), inputs.at(0).value());
ASSERT_EQ(concat->getInputs().at(Index{0}).value(), 80);
ASSERT_EQ(concat->getInputs().at(Index{2}).value(), 9);
ASSERT_THROW(concat->getInputs().at(Index{5}), std::out_of_range);
}
|
