1 //
2 // Copyright © 2023 Arm Ltd and contributors. 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 #include <armnn/utility/NumericCast.hpp>
13
14 #include <doctest/doctest.h>
15
16 #include <vector>
17
18 namespace
19 {
20
21 template<armnn::DataType ArmnnTypeInput>
CreateElementwiseBinaryNetwork(const TensorShape & input1Shape,const TensorShape & input2Shape,const TensorShape & outputShape,BinaryOperation operation,const float qScale=1.0f,const int32_t qOffset=0)22 INetworkPtr CreateElementwiseBinaryNetwork(const TensorShape& input1Shape,
23 const TensorShape& input2Shape,
24 const TensorShape& outputShape,
25 BinaryOperation 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 TensorInfo input1TensorInfo(input1Shape, ArmnnTypeInput, qScale, qOffset, true);
34 TensorInfo input2TensorInfo(input2Shape, ArmnnTypeInput, qScale, qOffset, true);
35 TensorInfo outputTensorInfo(outputShape, ArmnnTypeInput, qScale, qOffset);
36
37 IConnectableLayer* input1 = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(0));
38 IConnectableLayer* input2 = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(1));
39 IConnectableLayer* elementwiseBinaryLayer = net->AddElementwiseBinaryLayer(operation, "elementwiseUnary");
40 IConnectableLayer* output = net->AddOutputLayer(0, "output");
41
42 Connect(input1, elementwiseBinaryLayer, input1TensorInfo, 0, 0);
43 Connect(input2, elementwiseBinaryLayer, input2TensorInfo, 0, 1);
44 Connect(elementwiseBinaryLayer, output, outputTensorInfo, 0, 0);
45
46 return net;
47 }
48
49 template<armnn::DataType ArmnnInType,
50 typename TInput = armnn::ResolveType<ArmnnInType>>
ElementwiseBinarySimpleEndToEnd(const std::vector<BackendId> & backends,BinaryOperation operation)51 void ElementwiseBinarySimpleEndToEnd(const std::vector<BackendId>& backends,
52 BinaryOperation operation)
53 {
54 using namespace armnn;
55
56 const float qScale = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
57 const int32_t qOffset = IsQuantizedType<TInput>() ? 50 : 0;
58
59 const TensorShape& input1Shape = { 2, 2, 2, 2 };
60 const TensorShape& input2Shape = { 1 };
61 const TensorShape& outputShape = { 2, 2, 2, 2 };
62
63 // Builds up the structure of the network
64 INetworkPtr net = CreateElementwiseBinaryNetwork<ArmnnInType>(input1Shape, input2Shape, outputShape,
65 operation, qScale, qOffset);
66
67 CHECK(net);
68
69 const std::vector<float> input1({ 1, -1, 1, 1, 5, -5, 5, 5, -3, 3, 3, 3, 4, 4, -4, 4 });
70
71 const std::vector<float> input2({ 2 });
72 std::vector<float> expectedOutput;
73 switch (operation) {
74 case armnn::BinaryOperation::Add:
75 expectedOutput = { 3, 1, 3, 3, 7, -3, 7, 7, -1, 5, 5, 5, 6, 6, -2, 6 };
76 break;
77 case armnn::BinaryOperation::Div:
78 expectedOutput = {0.5f, -0.5f, 0.5f, 0.5f, 2.5f, -2.5f, 2.5f, 2.5f, -1.5f, 1.5f, 1.5f, 1.5f, 2, 2, -2, 2};
79 break;
80 case armnn::BinaryOperation::Maximum:
81 expectedOutput = { 2, 2, 2, 2, 5, 2, 5, 5, 2, 3, 3, 3, 4, 4, 2, 4 };
82 break;
83 case armnn::BinaryOperation::Minimum:
84 expectedOutput = { 1, -1, 1, 1, 2, -5, 2, 2, -3, 2, 2, 2, 2, 2, -4, 2 };
85 break;
86 case armnn::BinaryOperation::Mul:
87 expectedOutput = { 2, -2, 2, 2, 10, -10, 10, 10, -6, 6, 6, 6, 8, 8, -8, 8 };
88 break;
89 case armnn::BinaryOperation::Sub:
90 expectedOutput = { -1, -3, -1, -1, 3, -7, 3, 3, -5, 1, 1, 1, 2, 2, -6, 2 };
91 break;
92 default:
93 throw("Invalid Elementwise Binary operation");
94 }
95 const std::vector<float> expectedOutput_const = expectedOutput;
96 // quantize data
97 std::vector<TInput> qInput1Data = armnnUtils::QuantizedVector<TInput>(input1, qScale, qOffset);
98 std::vector<TInput> qInput2Data = armnnUtils::QuantizedVector<TInput>(input2, qScale, qOffset);
99 std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput_const, qScale, qOffset);
100
101 std::map<int, std::vector<TInput>> inputTensorData = {{ 0, qInput1Data }, { 1, qInput2Data }};
102 std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
103
104 EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(std::move(net), inputTensorData, expectedOutputData, backends);
105 }
106
107 } // anonymous namespace
108