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
|
//
// Copyright © 2019 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "CommonTestUtils.hpp"
#include <ResolveType.hpp>
#include <armnn/INetwork.hpp>
#include <boost/test/unit_test.hpp>
#include <vector>
namespace
{
template<armnn::DataType ArmnnTypeInput>
INetworkPtr CreateElementwiseUnaryNetwork(const TensorShape& inputShape,
const TensorShape& outputShape,
UnaryOperation operation,
const float qScale = 1.0f,
const int32_t qOffset = 0)
{
using namespace armnn;
INetworkPtr net(INetwork::Create());
ElementwiseUnaryDescriptor descriptor(operation);
IConnectableLayer* elementwiseUnaryLayer = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary");
TensorInfo inputTensorInfo(inputShape, ArmnnTypeInput, qScale, qOffset);
IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(0));
Connect(input, elementwiseUnaryLayer, inputTensorInfo, 0, 0);
TensorInfo outputTensorInfo(outputShape, ArmnnTypeInput, qScale, qOffset);
IConnectableLayer* output = net->AddOutputLayer(0, "output");
Connect(elementwiseUnaryLayer, output, outputTensorInfo, 0, 0);
return net;
}
template<armnn::DataType ArmnnInType,
typename TInput = armnn::ResolveType<ArmnnInType>>
void ElementwiseUnarySimpleEndToEnd(const std::vector<BackendId>& backends,
UnaryOperation operation,
const std::vector<float> expectedOutput)
{
using namespace armnn;
const float qScale = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<TInput>() ? 50 : 0;
const TensorShape& inputShape = { 2, 2, 2, 2 };
const TensorShape& outputShape = { 2, 2, 2, 2 };
// Builds up the structure of the network
INetworkPtr net = CreateElementwiseUnaryNetwork<ArmnnInType>(inputShape, outputShape, operation, qScale, qOffset);
BOOST_TEST_CHECKPOINT("create a network");
const std::vector<float> input({ 1, -1, 1, 1, 5, -5, 5, 5,
-3, 3, 3, 3, 4, 4, -4, 4 });
// quantize data
std::vector<TInput> qInputData = armnnUtils::QuantizedVector<TInput>(input, qScale, qOffset);
std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput, qScale, qOffset);
std::map<int, std::vector<TInput>> inputTensorData = {{ 0, qInputData }};
std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(move(net), inputTensorData, expectedOutputData, backends);
}
} // anonymous namespace
|
