1 //
2 // Copyright © 2022 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>
CreateSliceNetwork(const armnn::TensorShape & inputShape,const armnn::TensorShape & outputShape,const armnn::SliceDescriptor & descriptor,const float qScale=1.0f,const int32_t qOffset=0)18 armnn::INetworkPtr CreateSliceNetwork(const armnn::TensorShape& inputShape,
19 const armnn::TensorShape& outputShape,
20 const armnn::SliceDescriptor& 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* slice = network->AddSliceLayer(descriptor, "slice");
33 IConnectableLayer* input = network->AddInputLayer(0, "input");
34 IConnectableLayer* output = network->AddOutputLayer(0, "output");
35
36 Connect(input, slice, inputTensorInfo, 0, 0);
37 Connect(slice, output, outputTensorInfo, 0, 0);
38
39 return network;
40 }
41
42 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
SliceEndToEnd(const std::vector<armnn::BackendId> & backends)43 void SliceEndToEnd(const std::vector<armnn::BackendId>& backends)
44 {
45 using namespace armnn;
46
47 const TensorShape& inputShape = { 3, 2, 3 };
48 const TensorShape& outputShape = { 2, 1, 3 };
49
50 SliceDescriptor descriptor;
51 descriptor.m_Begin = { 1, 0, 0 };
52 descriptor.m_Size = { 2, 1, 3 };
53
54 INetworkPtr network = CreateSliceNetwork<ArmnnType>(inputShape, outputShape, descriptor);
55
56 CHECK(network);
57
58 std::vector<T> inputData{ 1, 1, 1, 2, 2, 2,
59 3, 3, 3, 4, 4, 4,
60 5, 5, 5, 6, 6, 6 };
61 std::vector<T> expectedOutput{ 3, 3, 3,
62 5, 5, 5 };
63
64 std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };
65 std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } };
66
67 EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
68 }
69
70 template<armnn::DataType ArmnnType>
SliceEndToEndFloat16(const std::vector<armnn::BackendId> & backends)71 void SliceEndToEndFloat16(const std::vector<armnn::BackendId>& backends)
72 {
73 using namespace armnn;
74 using namespace half_float::literal;
75 using Half = half_float::half;
76
77 const TensorShape& inputShape = { 3, 2, 3 };
78 const TensorShape& outputShape = { 2, 1, 3 };
79
80 SliceDescriptor descriptor;
81 descriptor.m_Begin = { 1, 0, 0 };
82 descriptor.m_Size = { 2, 1, 3 };
83
84 INetworkPtr network = CreateSliceNetwork<ArmnnType>(inputShape, outputShape, descriptor);
85 CHECK(network);
86
87 std::vector<Half> inputData{ 1._h, 1._h, 1._h, 2._h, 2._h, 2._h,
88 3._h, 3._h, 3._h, 4._h, 4._h, 4._h,
89 5._h, 5._h, 5._h, 6._h, 6._h, 6._h };
90 std::vector<Half> expectedOutput{ 3._h, 3._h, 3._h,
91 5._h, 5._h, 5._h };
92
93 std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } };
94 std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } };
95
96 EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
97 }
98
99 } // anonymous namespace