Aron Virginas-Tar | f97f6da | 2019-10-01 18:35:44 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2019 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <ResolveType.hpp> |
| 9 | |
Aron Virginas-Tar | f97f6da | 2019-10-01 18:35:44 +0100 | [diff] [blame] | 10 | |
Colm Donelan | c42a987 | 2022-02-02 16:35:09 +0000 | [diff] [blame] | 11 | #include <armnnUtils/QuantizeHelper.hpp> |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 12 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 13 | #include <armnnTestUtils/DataLayoutUtils.hpp> |
Aron Virginas-Tar | f97f6da | 2019-10-01 18:35:44 +0100 | [diff] [blame] | 14 | |
| 15 | namespace |
| 16 | { |
| 17 | |
| 18 | armnn::INetworkPtr CreateDepthToSpaceNetwork(const armnn::TensorInfo& inputInfo, |
| 19 | const armnn::TensorInfo& outputInfo, |
| 20 | const armnn::DepthToSpaceDescriptor& descriptor) |
| 21 | { |
| 22 | using namespace armnn; |
| 23 | |
| 24 | INetworkPtr network(INetwork::Create()); |
| 25 | |
| 26 | IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| 27 | IConnectableLayer* depthToSpace = network->AddDepthToSpaceLayer(descriptor, "depthToSpace"); |
| 28 | IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| 29 | |
| 30 | Connect(input, depthToSpace, inputInfo, 0, 0); |
| 31 | Connect(depthToSpace, output, outputInfo, 0, 0); |
| 32 | |
| 33 | return network; |
| 34 | } |
| 35 | |
| 36 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 37 | void DepthToSpaceEndToEndImpl(const std::vector<armnn::BackendId>& backends, |
| 38 | const DepthToSpaceDescriptor& descriptor, |
| 39 | const armnn::TensorShape& nhwcInputShape, |
| 40 | const armnn::TensorShape& nhwcOutputShape, |
| 41 | const std::vector<float>& floatInputData, |
| 42 | const std::vector<float>& floatExpectedOutputData) |
| 43 | { |
| 44 | using namespace armnn; |
| 45 | |
| 46 | TensorInfo inputInfo(nhwcInputShape, ArmnnType); |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 47 | inputInfo.SetConstant(true); |
Aron Virginas-Tar | f97f6da | 2019-10-01 18:35:44 +0100 | [diff] [blame] | 48 | TensorInfo outputInfo(nhwcOutputShape, ArmnnType); |
| 49 | |
| 50 | constexpr float qScale = 0.25f; |
| 51 | constexpr int32_t qOffset = 128; |
| 52 | |
| 53 | // Set quantization parameters for quantized types |
| 54 | if (IsQuantizedType<T>()) |
| 55 | { |
| 56 | inputInfo.SetQuantizationScale(qScale); |
| 57 | inputInfo.SetQuantizationOffset(qOffset); |
| 58 | outputInfo.SetQuantizationScale(qScale); |
| 59 | outputInfo.SetQuantizationOffset(qOffset); |
| 60 | } |
| 61 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 62 | std::vector<T> inputData = armnnUtils::QuantizedVector<T>(floatInputData, qScale, qOffset); |
| 63 | std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData, qScale, qOffset); |
Aron Virginas-Tar | f97f6da | 2019-10-01 18:35:44 +0100 | [diff] [blame] | 64 | |
| 65 | // Permute tensors from NHWC to NCHW (if needed) |
| 66 | if (descriptor.m_DataLayout == DataLayout::NCHW) |
| 67 | { |
| 68 | PermuteTensorNhwcToNchw(inputInfo, inputData); |
| 69 | PermuteTensorNhwcToNchw(outputInfo, expectedOutputData); |
| 70 | } |
| 71 | |
| 72 | INetworkPtr network = CreateDepthToSpaceNetwork(inputInfo, outputInfo, descriptor); |
| 73 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| 74 | { { 0, inputData } }, |
| 75 | { { 0, expectedOutputData } }, |
| 76 | backends); |
| 77 | } |
| 78 | |
| 79 | } // anonymous namespace |
| 80 | |
| 81 | template<armnn::DataType ArmnnType> |
| 82 | void DepthToSpaceEndToEnd(const std::vector<armnn::BackendId>& defaultBackends, |
| 83 | armnn::DataLayout dataLayout) |
| 84 | { |
| 85 | using namespace armnn; |
| 86 | |
| 87 | TensorShape inputShape = { 2, 2, 2, 4 }; |
| 88 | TensorShape outputShape = { 2, 4, 4, 1 }; |
| 89 | |
| 90 | std::vector<float> inputData = |
| 91 | { |
| 92 | 1.f, 2.f, 3.f, 4.f, |
| 93 | 5.f, 6.f, 7.f, 8.f, |
| 94 | 9.f, 10.f, 11.f, 12.f, |
| 95 | 13.f, 14.f, 15.f, 16.f, |
| 96 | |
| 97 | 17.f, 18.f, 19.f, 20.f, |
| 98 | 21.f, 22.f, 23.f, 24.f, |
| 99 | 25.f, 26.f, 27.f, 28.f, |
| 100 | 29.f, 30.f, 31.f, 32.f |
| 101 | }; |
| 102 | |
| 103 | std::vector<float> expectedOutputData = |
| 104 | { |
| 105 | 1.f, 2.f, 5.f, 6.f, |
| 106 | 3.f, 4.f, 7.f, 8.f, |
| 107 | 9.f, 10.f, 13.f, 14.f, |
| 108 | 11.f, 12.f, 15.f, 16.f, |
| 109 | |
| 110 | 17.f, 18.f, 21.f, 22.f, |
| 111 | 19.f, 20.f, 23.f, 24.f, |
| 112 | 25.f, 26.f, 29.f, 30.f, |
| 113 | 27.f, 28.f, 31.f, 32.f |
| 114 | }; |
| 115 | |
| 116 | DepthToSpaceEndToEndImpl<ArmnnType>(defaultBackends, |
| 117 | DepthToSpaceDescriptor(2, dataLayout), |
| 118 | inputShape, |
| 119 | outputShape, |
| 120 | inputData, |
| 121 | expectedOutputData); |
| 122 | } |