2 * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
8 * http://www.apache.org/licenses/LICENSE-2.0
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
17 #include "NodeExecution.h"
19 #include "locomotiv/NodeData.h"
20 #include "NodeDataImpl.h"
21 #include "NodeDomain.h"
23 #include <nncc/core/ADT/tensor/Shape.h>
24 #include <nncc/core/ADT/tensor/Buffer.h>
25 #include <nncc/core/ADT/tensor/LexicalLayout.h>
27 #include <gtest/gtest.h>
29 using nncc::core::ADT::tensor::Index;
30 using nncc::core::ADT::tensor::Shape;
31 using nncc::core::ADT::tensor::LexicalLayout;
32 using nncc::core::ADT::tensor::make_buffer;
34 TEST(NodeExecution_TensorConcat, f32)
36 // Make (pull, pull)-concat graph
37 auto g = loco::make_graph();
38 auto pull_l = g->nodes()->create<loco::Pull>();
39 pull_l->dtype(loco::DataType::FLOAT32);
40 pull_l->shape({1, 2});
41 auto pull_r = g->nodes()->create<loco::Pull>();
42 pull_r->dtype(loco::DataType::FLOAT32);
43 pull_r->shape({1, 2});
44 auto tconcat = g->nodes()->create<loco::TensorConcat>();
49 // Make and assign data to pull node
50 auto pull_l_buf = make_buffer<float, LexicalLayout>(Shape{1, 2});
51 pull_l_buf.at(Index{0, 0}) = -1.0f;
52 pull_l_buf.at(Index{0, 1}) = -2.0f;
53 auto pull_r_buf = make_buffer<float, LexicalLayout>(Shape{1, 2});
54 pull_r_buf.at(Index{0, 0}) = 3.0f;
55 pull_r_buf.at(Index{0, 1}) = 4.0f;
57 auto pull_l_data = locomotiv::make_data(pull_l_buf);
58 locomotiv::annot_data(pull_l, std::move(pull_l_data));
59 locomotiv::annot_domain(pull_l, loco::Domain::Tensor);
60 auto pull_r_data = locomotiv::make_data(pull_r_buf);
61 locomotiv::annot_data(pull_r, std::move(pull_r_data));
62 locomotiv::annot_domain(pull_r, loco::Domain::Tensor);
64 locomotiv::NodeExecution::get().run(tconcat);
66 auto concat_data = locomotiv::annot_data(tconcat);
67 ASSERT_NE(concat_data, nullptr);
68 ASSERT_EQ(loco::DataType::FLOAT32, concat_data->dtype());
69 ASSERT_EQ((Shape{2, 2}), (*(concat_data->shape())));
70 ASSERT_FLOAT_EQ(-1.0f, concat_data->as_f32_bufptr()->at(Index{0, 0}));
71 ASSERT_FLOAT_EQ(-2.0f, concat_data->as_f32_bufptr()->at(Index{0, 1}));
72 ASSERT_FLOAT_EQ(3.0f, concat_data->as_f32_bufptr()->at(Index{1, 0}));
73 ASSERT_FLOAT_EQ(4.0f, concat_data->as_f32_bufptr()->at(Index{1, 1}));
75 ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(tconcat));
78 TEST(NodeExecution_TensorConcat, f32_2)
80 // Make (pull, pull)-concat graph
81 auto g = loco::make_graph();
82 auto pull_l = g->nodes()->create<loco::Pull>();
83 pull_l->dtype(loco::DataType::FLOAT32);
84 pull_l->shape({1, 2});
85 auto pull_r = g->nodes()->create<loco::Pull>();
86 pull_r->dtype(loco::DataType::FLOAT32);
87 pull_r->shape({3, 2});
88 auto tconcat = g->nodes()->create<loco::TensorConcat>();
93 // Make and assign data to pull node
94 auto pull_l_buf = make_buffer<float, LexicalLayout>(Shape{1, 2});
95 pull_l_buf.at(Index{0, 0}) = -1.0f;
96 pull_l_buf.at(Index{0, 1}) = -2.0f;
97 auto pull_r_buf = make_buffer<float, LexicalLayout>(Shape{3, 2});
98 pull_r_buf.at(Index{0, 0}) = 3.0f;
99 pull_r_buf.at(Index{0, 1}) = 4.0f;
100 pull_r_buf.at(Index{1, 0}) = -3.0f;
101 pull_r_buf.at(Index{1, 1}) = -4.0f;
102 pull_r_buf.at(Index{2, 0}) = 5.0f;
103 pull_r_buf.at(Index{2, 1}) = 6.0f;
105 auto pull_l_data = locomotiv::make_data(pull_l_buf);
106 locomotiv::annot_data(pull_l, std::move(pull_l_data));
107 locomotiv::annot_domain(pull_l, loco::Domain::Tensor);
108 auto pull_r_data = locomotiv::make_data(pull_r_buf);
109 locomotiv::annot_data(pull_r, std::move(pull_r_data));
110 locomotiv::annot_domain(pull_r, loco::Domain::Tensor);
112 locomotiv::NodeExecution::get().run(tconcat);
114 auto concat_data = locomotiv::annot_data(tconcat);
115 ASSERT_NE(concat_data, nullptr);
116 ASSERT_EQ(loco::DataType::FLOAT32, concat_data->dtype());
117 ASSERT_EQ((Shape{4, 2}), (*(concat_data->shape())));
118 ASSERT_FLOAT_EQ(-1.0f, concat_data->as_f32_bufptr()->at(Index{0, 0}));
119 ASSERT_FLOAT_EQ(-2.0f, concat_data->as_f32_bufptr()->at(Index{0, 1}));
120 ASSERT_FLOAT_EQ(3.0f, concat_data->as_f32_bufptr()->at(Index{1, 0}));
121 ASSERT_FLOAT_EQ(4.0f, concat_data->as_f32_bufptr()->at(Index{1, 1}));
122 ASSERT_FLOAT_EQ(-3.0f, concat_data->as_f32_bufptr()->at(Index{2, 0}));
123 ASSERT_FLOAT_EQ(-4.0f, concat_data->as_f32_bufptr()->at(Index{2, 1}));
124 ASSERT_FLOAT_EQ(5.0f, concat_data->as_f32_bufptr()->at(Index{3, 0}));
125 ASSERT_FLOAT_EQ(6.0f, concat_data->as_f32_bufptr()->at(Index{3, 1}));
127 ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(tconcat));