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
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
|
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "WorkloadTestUtils.hpp"
#include <armnn/ArmNN.hpp>
#include <armnn/Tensor.hpp>
#include <armnn/TypesUtils.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/IBackendInternal.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
#include <test/TensorHelpers.hpp>
namespace
{
template<typename T, std::size_t Dim>
LayerTestResult<float, Dim> DequantizeTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::TensorInfo& inputTensorInfo,
const armnn::TensorInfo& outputTensorInfo,
const std::vector<T>& inputData,
const std::vector<float>& expectedOutputData,
armnn::DequantizeQueueDescriptor descriptor)
{
boost::multi_array<T, Dim> input = MakeTensor<T, Dim>(inputTensorInfo, inputData);
LayerTestResult<float, Dim> ret(outputTensorInfo);
ret.outputExpected = MakeTensor<float, Dim>(outputTensorInfo, expectedOutputData);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDequantize(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), input.data());
ExecuteWorkload(*workload, memoryManager);
CopyDataFromITensorHandle(ret.output.data(), outputHandle.get());
return ret;
}
template <armnn::DataType ArmnnInputType>
LayerTestResult<float, 4> DequantizeSimpleTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
{
using T = armnn::ResolveType<ArmnnInputType>;
armnn::DequantizeQueueDescriptor desc;
const armnn::TensorInfo inputTensorInfo({1, 2, 2, 3}, ArmnnInputType, 0.5f, 0);
const armnn::TensorInfo outputTensorInfo({1, 2, 2, 3}, armnn::DataType::Float32);
std::vector<T> inputData = std::vector<T>(
{
2, 4, 6,
8, 10, 12,
14, 16, 18,
20, 22, 24,
});
std::vector<float> expectedOutputData = std::vector<float>(
{
1.0f, 2.0f, 3.0f,
4.0f, 5.0f, 6.0f,
7.0f, 8.0f, 9.0f,
10.0f, 11.0f, 12.0f,
});
return DequantizeTestImpl<T, 4>(workloadFactory,
memoryManager,
inputTensorInfo,
outputTensorInfo,
inputData,
expectedOutputData,
desc);
}
template <armnn::DataType ArmnnInputType>
LayerTestResult<float, 4> DequantizeOffsetTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
{
using T = armnn::ResolveType<ArmnnInputType>;
armnn::DequantizeQueueDescriptor desc;
const armnn::TensorInfo inputTensorInfo({1, 2, 2, 3}, ArmnnInputType, 0.5f, 1);
const armnn::TensorInfo outputTensorInfo({1, 2, 2, 3}, armnn::DataType::Float32);
std::vector<T> inputData = std::vector<T>(
{
3, 5, 7,
9, 11, 13,
15, 17, 19,
21, 23, 25,
});
std::vector<float> expectedOutputData = std::vector<float>(
{
1.0f, 2.0f, 3.0f,
4.0f, 5.0f, 6.0f,
7.0f, 8.0f, 9.0f,
10.0f, 11.0f, 12.0f,
});
return DequantizeTestImpl<T, 4>(workloadFactory,
memoryManager,
inputTensorInfo,
outputTensorInfo,
inputData,
expectedOutputData,
desc);
}
} // anonymous namespace
|
