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
|
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "CommonTestUtils.hpp"
#include <QuantizeHelper.hpp>
#include <ResolveType.hpp>
#include <armnn/ArmNN.hpp>
namespace
{
armnn::INetworkPtr CreateAbsNetwork(const armnn::TensorInfo& tensorInfo)
{
armnn::INetworkPtr network(armnn::INetwork::Create());
armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "input");
armnn::IConnectableLayer* absLayer = network->AddAbsLayer("abs");
armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "output");
Connect(inputLayer, absLayer, tensorInfo, 0, 0);
Connect(absLayer, outputLayer, tensorInfo, 0, 0);
return network;
}
} // anonymous namespace
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void AbsEndToEnd(const std::vector<armnn::BackendId>& backends)
{
using namespace armnn;
const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
TensorInfo tensorInfo({ 1, 1, 2, 3 }, ArmnnType, qScale, qOffset);
std::vector<float> inputData =
{
-1.f, 2.f, -3.f,
4.f, -5.f, 6.f
};
std::vector<float> expectedOutputData =
{
1.f, 2.f, 3.f,
4.f, 5.f, 6.f
};
// quantize data
std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset);
INetworkPtr network = CreateAbsNetwork(tensorInfo);
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
{ { 0, qInputData } },
{ { 0, qExpectedOutputData } },
backends);
}
|
