1 //
2 // Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 #pragma once
6
7 #include <armnn/INetwork.hpp>
8
9 #include <CommonTestUtils.hpp>
10 #include <ResolveType.hpp>
11
12 #include <doctest/doctest.h>
13
14 namespace
15 {
16
17 template<typename armnn::DataType DataType>
CreateReduceNetwork(const armnn::TensorShape & inputShape,const armnn::TensorShape & outputShape,const armnn::ReduceDescriptor & descriptor,const float qScale=1.0f,const int32_t qOffset=0)18 armnn::INetworkPtr CreateReduceNetwork(const armnn::TensorShape& inputShape,
19 const armnn::TensorShape& outputShape,
20 const armnn::ReduceDescriptor& descriptor,
21 const float qScale = 1.0f,
22 const int32_t qOffset = 0)
23 {
24 using namespace armnn;
25
26 INetworkPtr network(INetwork::Create());
27
28 TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true);
29 TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
30
31
32 IConnectableLayer* reduce = network->AddReduceLayer(descriptor, "reduce");
33 IConnectableLayer* input = network->AddInputLayer(0, "input");
34 IConnectableLayer* output = network->AddOutputLayer(0, "output");
35
36 Connect(input, reduce, inputTensorInfo, 0, 0);
37 Connect(reduce, output, outputTensorInfo, 0, 0);
38
39 return network;
40 }
41
42 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
ReduceEndToEnd(const std::vector<armnn::BackendId> & backends)43 void ReduceEndToEnd(const std::vector<armnn::BackendId>& backends)
44 {
45 using namespace armnn;
46
47 const TensorShape& inputShape = { 1, 1, 1, 5 };
48 const TensorShape& outputShape = { 1, 1, 1 };
49
50 ReduceDescriptor descriptor;
51 descriptor.m_KeepDims = false;
52 descriptor.m_vAxis = { 3 };
53 descriptor.m_ReduceOperation = ReduceOperation::Sum;
54
55 INetworkPtr network = CreateReduceNetwork<ArmnnType>(inputShape, outputShape, descriptor);
56
57 CHECK(network);
58
59 std::vector<float> floatInputData({ 5.0f, 2.0f, 8.0f, 10.0f, 9.0f });
60 std::vector<float> floatOutputData({ 34.0f });
61
62 std::vector<T> inputData = armnnUtils::QuantizedVector<T>(floatInputData);
63 std::vector<T> outputData = armnnUtils::QuantizedVector<T>(floatOutputData);
64
65 std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };
66 std::map<int, std::vector<T>> expectedOutputData = { { 0, outputData } };
67
68 EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
69 }
70 } // anonymous namespace
71