1 //
2 // Copyright © 2020-2021, 2023 Arm Ltd. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 #pragma once
6
7 #include <CommonTestUtils.hpp>
8
9 #include <ResolveType.hpp>
10
11 #include <armnn/INetwork.hpp>
12
13 #include <armnn/utility/NumericCast.hpp>
14
15 #include <doctest/doctest.h>
16
17 #include <vector>
18
19 namespace
20 {
21
22 template<armnn::DataType ArmnnTypeInput>
CreateElementwiseUnaryNetwork(const TensorShape & inputShape,const TensorShape & outputShape,UnaryOperation operation,const float qScale=1.0f,const int32_t qOffset=0)23 INetworkPtr CreateElementwiseUnaryNetwork(const TensorShape& inputShape,
24 const TensorShape& outputShape,
25 UnaryOperation operation,
26 const float qScale = 1.0f,
27 const int32_t qOffset = 0)
28 {
29 using namespace armnn;
30
31 INetworkPtr net(INetwork::Create());
32
33 ElementwiseUnaryDescriptor descriptor(operation);
34 IConnectableLayer* elementwiseUnaryLayer = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary");
35
36 TensorInfo inputTensorInfo(inputShape, ArmnnTypeInput, qScale, qOffset, true);
37 IConnectableLayer* input = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(0));
38 Connect(input, elementwiseUnaryLayer, inputTensorInfo, 0, 0);
39
40 TensorInfo outputTensorInfo(outputShape, ArmnnTypeInput, qScale, qOffset);
41 IConnectableLayer* output = net->AddOutputLayer(0, "output");
42 Connect(elementwiseUnaryLayer, output, outputTensorInfo, 0, 0);
43
44 return net;
45 }
46
47 template<armnn::DataType ArmnnInType,
48 typename TInput = armnn::ResolveType<ArmnnInType>>
ElementwiseUnarySimpleEndToEnd(const std::vector<BackendId> & backends,UnaryOperation operation)49 void ElementwiseUnarySimpleEndToEnd(const std::vector<BackendId>& backends,
50 UnaryOperation operation)
51 {
52 using namespace armnn;
53
54 const float qScale = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
55 const int32_t qOffset = IsQuantizedType<TInput>() ? 50 : 0;
56
57 const TensorShape& inputShape = { 2, 2, 2, 2 };
58 const TensorShape& outputShape = { 2, 2, 2, 2 };
59
60 // Builds up the structure of the network
61 INetworkPtr net = CreateElementwiseUnaryNetwork<ArmnnInType>(inputShape, outputShape, operation, qScale, qOffset);
62
63 CHECK(net);
64
65 std::vector<float> input;
66 std::vector<float> expectedOutput;
67 switch(operation)
68 {
69 case UnaryOperation::Abs:
70 input = { 1, -1, 1, 1, 5, -5, 5, 5,
71 -3, 3, 3, 3, 4, 4, -4, 4 };
72 expectedOutput = { 1.f, 1.f, 1.f, 1.f, 5.f, 5.f, 5.f, 5.f,
73 3.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 4.f };
74 break;
75 case UnaryOperation::Rsqrt:
76 input = { 1, 1, 1, 1, 5, 5, 5, 5,
77 3, 3, 3, 3, 4, 4, 4, 4 };
78 expectedOutput = { 1.f, 1.f, 1.f, 1.f, 0.447214f, 0.447214f, 0.447214f, 0.447214f,
79 0.57735f, 0.57735f, 0.57735f, 0.57735f, 0.5f, 0.5f, 0.5f, 0.5f };
80 break;
81 default:
82 input = { 1, -1, 1, 1, 5, -5, 5, 5,
83 -3, 3, 3, 3, 4, 4, -4, 4 };
84 expectedOutput = { 1.f, 1.f, 1.f, 1.f, 5.f, 5.f, 5.f, 5.f,
85 3.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 4.f };
86 break;
87 }
88
89
90 // quantize data
91 std::vector<TInput> qInputData = armnnUtils::QuantizedVector<TInput>(input, qScale, qOffset);
92 std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput, qScale, qOffset);
93
94 std::map<int, std::vector<TInput>> inputTensorData = {{ 0, qInputData }};
95 std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
96
97 EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(move(net), inputTensorData, expectedOutputData, backends);
98 }
99
100 } // anonymous namespace
101