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
|
/*
* 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_While)
{
// The model looks just like the below pseudocode
//
// function model(x)
// {
// while (x < 100.0)
// {
// x = x + 10.0;
// }
// return x
// }
CircleGen cgen;
std::vector<float> incr_data{10};
uint32_t incr_buf = cgen.addBuffer(incr_data);
std::vector<float> end_data{100};
uint32_t end_buf = cgen.addBuffer(end_data);
// primary subgraph
{
int x_in = cgen.addTensor({{1}, circle::TensorType_FLOAT32});
int x_out = cgen.addTensor({{1}, circle::TensorType_FLOAT32});
cgen.addOperatorWhile({{x_in}, {x_out}}, 1, 2);
cgen.setInputsAndOutputs({x_in}, {x_out});
}
// cond subgraph
{
cgen.nextSubgraph();
int x = cgen.addTensor({{1}, circle::TensorType_FLOAT32});
int end = cgen.addTensor({{1}, circle::TensorType_FLOAT32, end_buf});
int result = cgen.addTensor({{1}, circle::TensorType_BOOL});
cgen.addOperatorLess({{x, end}, {result}});
cgen.setInputsAndOutputs({x}, {result});
}
// body subgraph
{
cgen.nextSubgraph();
int x_in = cgen.addTensor({{1}, circle::TensorType_FLOAT32});
int incr = cgen.addTensor({{1}, circle::TensorType_FLOAT32, incr_buf});
int x_out = cgen.addTensor({{1}, circle::TensorType_FLOAT32});
cgen.addOperatorAdd({{x_in, incr}, {x_out}}, circle::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({x_in}, {x_out});
}
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->addTestCase({{{0}}, {{100}}});
_context->addTestCase({{{2}}, {{102}}});
_context->addTestCase({{{22}}, {{102}}});
_context->setBackends({"cpu"});
SUCCEED();
}
|
