surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4 | // |
| 5 | #include <boost/test/unit_test.hpp> |
| 6 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 7 | #include <armnn/ArmNN.hpp> |
| 8 | #include <Graph.hpp> |
| 9 | #include <Optimizer.hpp> |
| 10 | #include <backends/CpuTensorHandle.hpp> |
| 11 | #include <FloatingPointConverter.hpp> |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 12 | |
| 13 | namespace |
| 14 | { |
| 15 | template <typename LayerT> |
| 16 | bool IsLayerOfType(const armnn::Layer* const layer) |
| 17 | { |
| 18 | return (layer->GetType() == armnn::LayerEnumOf<LayerT>()); |
| 19 | } |
| 20 | |
| 21 | bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last) |
| 22 | { |
| 23 | return (first == last); |
| 24 | } |
| 25 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 26 | /// Checks each unary function in Us evaluates true for each correspondent layer in the sequence [first, last). |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 27 | template <typename U, typename... Us> |
| 28 | bool CheckSequence(const armnn::Graph::ConstIterator first, |
| 29 | const armnn::Graph::ConstIterator last, |
| 30 | U&& u, |
| 31 | Us&&... us) |
| 32 | { |
| 33 | return u(*first) && CheckSequence(std::next(first), last, us...); |
| 34 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 35 | |
| 36 | template <typename LayerT> |
| 37 | bool CheckRelatedLayers(armnn::Graph& graph, const std::list<std::string>& testRelatedLayers) |
| 38 | { |
| 39 | for (auto& layer : graph) |
| 40 | { |
| 41 | if (layer->GetType() == armnn::LayerEnumOf<LayerT>()) |
| 42 | { |
| 43 | auto& relatedLayers = layer->GetRelatedLayerNames(); |
| 44 | if(!std::equal(relatedLayers.begin(), relatedLayers.end(), |
| 45 | testRelatedLayers.begin(), testRelatedLayers.end())) |
| 46 | { |
| 47 | return false; |
| 48 | } |
| 49 | } |
| 50 | } |
| 51 | |
| 52 | return true; |
| 53 | } |
| 54 | |
| 55 | // connects two layers |
| 56 | using namespace armnn; |
| 57 | void Connect(Layer* from, Layer* to, const TensorInfo& tensorInfo, unsigned int fromIndex = 0, unsigned int toIndex = 0) |
| 58 | { |
| 59 | from->GetOutputSlot(fromIndex).Connect(to->GetInputSlot(toIndex)); |
| 60 | from->GetOutputHandler(fromIndex).SetTensorInfo(tensorInfo); |
| 61 | } |
| 62 | |
| 63 | void CreateLSTMLayerHelper(Graph &graph, bool CifgEnabled) |
| 64 | { |
| 65 | LstmDescriptor layerDesc; |
| 66 | layerDesc.m_ActivationFunc = 4; |
| 67 | layerDesc.m_ClippingThresCell = 0.2f; |
| 68 | layerDesc.m_ClippingThresProj = 0.4f; |
| 69 | layerDesc.m_CifgEnabled = CifgEnabled; |
| 70 | layerDesc.m_PeepholeEnabled = false; |
| 71 | layerDesc.m_ProjectionEnabled = false; |
| 72 | |
| 73 | LstmLayer* const layer = graph.AddLayer<LstmLayer>(layerDesc, "layer"); |
| 74 | unsigned int batchSize = 3; |
| 75 | unsigned int inputSize = 2; |
| 76 | unsigned int numUnits = 4; |
| 77 | unsigned int outputSize = 4; |
| 78 | |
| 79 | layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedCpuTensorHandle> |
| 80 | (TensorInfo({ numUnits, inputSize }, DataType::Float32)); |
| 81 | layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedCpuTensorHandle> |
| 82 | (TensorInfo({ numUnits, inputSize }, DataType::Float32)); |
| 83 | layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedCpuTensorHandle> |
| 84 | (TensorInfo({ numUnits, inputSize }, DataType::Float32)); |
| 85 | layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<ScopedCpuTensorHandle> |
| 86 | (TensorInfo({ numUnits, outputSize }, DataType::Float32)); |
| 87 | layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<ScopedCpuTensorHandle> |
| 88 | (TensorInfo({ numUnits, outputSize }, DataType::Float32)); |
| 89 | layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<ScopedCpuTensorHandle> |
| 90 | (TensorInfo({ numUnits, outputSize }, DataType::Float32)); |
| 91 | layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedCpuTensorHandle> |
| 92 | (TensorInfo({ numUnits }, DataType::Float32)); |
| 93 | layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedCpuTensorHandle> |
| 94 | (TensorInfo({ numUnits }, DataType::Float32)); |
| 95 | layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedCpuTensorHandle> |
| 96 | (TensorInfo({ numUnits }, DataType::Float32)); |
| 97 | |
| 98 | layer->m_BasicParameters.m_InputToForgetWeights->Allocate(); |
| 99 | layer->m_BasicParameters.m_InputToCellWeights->Allocate(); |
| 100 | layer->m_BasicParameters.m_InputToOutputWeights->Allocate(); |
| 101 | layer->m_BasicParameters.m_RecurrentToForgetWeights->Allocate(); |
| 102 | layer->m_BasicParameters.m_RecurrentToCellWeights->Allocate(); |
| 103 | layer->m_BasicParameters.m_RecurrentToOutputWeights->Allocate(); |
| 104 | layer->m_BasicParameters.m_ForgetGateBias->Allocate(); |
| 105 | layer->m_BasicParameters.m_CellBias->Allocate(); |
| 106 | layer->m_BasicParameters.m_OutputGateBias->Allocate(); |
| 107 | |
| 108 | if (!layerDesc.m_CifgEnabled) |
| 109 | { |
| 110 | layer->m_CifgParameters.m_InputToInputWeights = std::make_unique<ScopedCpuTensorHandle> |
| 111 | (TensorInfo({ numUnits, inputSize }, DataType::Float32)); |
| 112 | layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<ScopedCpuTensorHandle> |
| 113 | (TensorInfo({ numUnits, outputSize }, DataType::Float32)); |
| 114 | layer->m_CifgParameters.m_CellToInputWeights = std::make_unique<ScopedCpuTensorHandle> |
| 115 | (TensorInfo({ numUnits }, DataType::Float32)); |
| 116 | layer->m_CifgParameters.m_InputGateBias = std::make_unique<ScopedCpuTensorHandle> |
| 117 | (TensorInfo({ numUnits }, DataType::Float32)); |
| 118 | layer->m_CifgParameters.m_InputToInputWeights->Allocate(); |
| 119 | layer->m_CifgParameters.m_RecurrentToInputWeights->Allocate(); |
| 120 | layer->m_CifgParameters.m_CellToInputWeights->Allocate(); |
| 121 | layer->m_CifgParameters.m_InputGateBias->Allocate(); |
| 122 | } |
| 123 | |
| 124 | if (layerDesc.m_ProjectionEnabled) |
| 125 | { |
| 126 | layer->m_ProjectionParameters.m_ProjectionWeights = std::make_unique<ScopedCpuTensorHandle> |
| 127 | (TensorInfo({ outputSize, numUnits }, DataType::Float32)); |
| 128 | layer->m_ProjectionParameters.m_ProjectionBias = std::make_unique<ScopedCpuTensorHandle> |
| 129 | (TensorInfo({ outputSize }, DataType::Float32)); |
| 130 | layer->m_ProjectionParameters.m_ProjectionWeights->Allocate(); |
| 131 | layer->m_ProjectionParameters.m_ProjectionBias->Allocate(); |
| 132 | } |
| 133 | |
| 134 | if (layerDesc.m_PeepholeEnabled) |
| 135 | { |
| 136 | layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<ScopedCpuTensorHandle> |
| 137 | (TensorInfo({ numUnits }, DataType::Float32)); |
| 138 | layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<ScopedCpuTensorHandle> |
| 139 | (TensorInfo({ numUnits }, DataType::Float32)); |
| 140 | layer->m_PeepholeParameters.m_CellToForgetWeights->Allocate(); |
| 141 | layer->m_PeepholeParameters.m_CellToOutputWeights->Allocate(); |
| 142 | } |
| 143 | |
| 144 | // create input and output layers |
| 145 | Layer* const input = graph.AddLayer<InputLayer>(0, "input"); |
| 146 | Layer* const outputStateIn = graph.AddLayer<InputLayer>(1, "outputStateIn"); |
| 147 | Layer* const cellStateIn = graph.AddLayer<InputLayer>(2, "cellStateIn"); |
| 148 | Layer* const scratchBuffer = graph.AddLayer<OutputLayer>(0, "scratchBuffer"); |
| 149 | Layer* const outputStateOut = graph.AddLayer<OutputLayer>(1, "outputStateOut"); |
| 150 | Layer* const cellStateOut = graph.AddLayer<OutputLayer>(2, "cellStateOut"); |
| 151 | Layer* const output = graph.AddLayer<OutputLayer>(3, "output"); |
| 152 | |
| 153 | // connect up |
| 154 | armnn::TensorInfo lstmTensorInfo1({ batchSize, inputSize }, DataType::Float32); |
| 155 | armnn::TensorInfo lstmTensorInfo2({ batchSize, numUnits}, DataType::Float32); |
| 156 | armnn::TensorInfo lstmTensorInfo3({ batchSize, outputSize }, DataType::Float32); |
| 157 | armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits*3 }, DataType::Float32); |
| 158 | if (layerDesc.m_CifgEnabled) |
| 159 | { |
| 160 | lstmTensorInfoScratchBuff.SetShape({ batchSize, numUnits*4 }); |
| 161 | } |
| 162 | |
| 163 | Connect(input, layer, lstmTensorInfo1, 0, 0); |
| 164 | Connect(cellStateIn, layer, lstmTensorInfo2, 0, 1); |
| 165 | Connect(outputStateIn, layer, lstmTensorInfo3, 0, 2); |
| 166 | Connect(layer, scratchBuffer, lstmTensorInfoScratchBuff, 0, 0); |
| 167 | Connect(layer, outputStateOut, lstmTensorInfo3, 1, 0); |
| 168 | Connect(layer, cellStateOut, lstmTensorInfo2, 2, 0); |
| 169 | Connect(layer, output, lstmTensorInfo3, 3, 0); |
| 170 | } |
| 171 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 172 | } |
| 173 | |
| 174 | BOOST_AUTO_TEST_SUITE(Optimizer) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 175 | using namespace armnn::optimizations; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 176 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 177 | BOOST_AUTO_TEST_CASE(OptimizeInversePermutesTest) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 178 | { |
| 179 | armnn::Graph graph; |
| 180 | |
| 181 | auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); |
| 182 | |
| 183 | graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input"); |
| 184 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 185 | // Inserts two permutes, one the inverse of the other. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 186 | graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), |
| 187 | armnn::PermuteDescriptor({0, 2, 3, 1}), |
| 188 | "perm0231"); |
| 189 | graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), |
| 190 | armnn::PermuteDescriptor({0, 3, 1, 2}), |
| 191 | "perm0312"); |
| 192 | |
| 193 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 194 | graph.cend(), |
| 195 | &IsLayerOfType<armnn::InputLayer>, |
| 196 | &IsLayerOfType<armnn::PermuteLayer>, |
| 197 | &IsLayerOfType<armnn::PermuteLayer>, |
| 198 | &IsLayerOfType<armnn::OutputLayer>)); |
| 199 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 200 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(OptimizeInversePermutes())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 201 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 202 | // The permutes are removed. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 203 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 204 | graph.cend(), |
| 205 | &IsLayerOfType<armnn::InputLayer>, |
| 206 | &IsLayerOfType<armnn::OutputLayer>)); |
| 207 | } |
| 208 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 209 | BOOST_AUTO_TEST_CASE(LSTMValidateTensorShapesFromInputsCIFGDisabledTest) |
| 210 | { |
| 211 | Graph graph; |
| 212 | |
| 213 | //Helper function creates graph containing LSTM layer with required input and output layers |
| 214 | CreateLSTMLayerHelper(graph, false); |
| 215 | |
| 216 | //This function used to call ValidateShapesFromInputs(); |
| 217 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 218 | } |
| 219 | |
| 220 | BOOST_AUTO_TEST_CASE(LSTMValidateTensorShapesFromInputsCIFGEnabledTest) |
| 221 | { |
| 222 | Graph graph; |
| 223 | |
| 224 | //Helper function creates graph containing LSTM layer with required input and output layers |
| 225 | CreateLSTMLayerHelper(graph, true); |
| 226 | |
| 227 | //This function used to call ValidateShapesFromInputs(); |
| 228 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 229 | } |
| 230 | |
| 231 | BOOST_AUTO_TEST_CASE(MovePermuteUpTest) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 232 | { |
| 233 | const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32); |
| 234 | const armnn::TensorInfo permuted({ 1, 3, 5, 2 }, armnn::DataType::Float32); |
| 235 | |
| 236 | armnn::Graph graph; |
| 237 | |
| 238 | armnn::LayerBindingId inputId = 0; |
| 239 | |
| 240 | armnn::Layer* head = graph.AddLayer<armnn::OutputLayer>(0, "output"); |
| 241 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 242 | std::string permuteLayerName = "original_permute"; |
| 243 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 244 | // Insert permute |
| 245 | head = graph.InsertNewLayer<armnn::PermuteLayer>(head->GetInputSlot(0), |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 246 | armnn::PermuteDescriptor({ 0, 2, 3, 1 }), |
| 247 | permuteLayerName.c_str()); |
| 248 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 249 | head->GetOutputHandler().SetTensorInfo(permuted); |
| 250 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 251 | // Inserts layers that don't care about data format. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 252 | head = graph.InsertNewLayer<armnn::ActivationLayer>(head->GetInputSlot(0), |
| 253 | armnn::ActivationDescriptor{}, ""); |
| 254 | head->GetOutputHandler().SetTensorInfo(info); |
| 255 | |
| 256 | head = graph.InsertNewLayer<armnn::AdditionLayer>(head->GetInputSlot(0), ""); |
| 257 | head->GetOutputHandler().SetTensorInfo(info); |
| 258 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 259 | // Inserts input for 2nd input of Addition. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 260 | graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(1), inputId++, "") |
| 261 | ->GetOutputHandler().SetTensorInfo(info); |
| 262 | |
| 263 | head = graph.InsertNewLayer<armnn::FakeQuantizationLayer>(head->GetInputSlot(0), |
| 264 | armnn::FakeQuantizationDescriptor{}, ""); |
| 265 | head->GetOutputHandler().SetTensorInfo(info); |
| 266 | |
| 267 | head = graph.InsertNewLayer<armnn::FloorLayer>(head->GetInputSlot(0), ""); |
| 268 | head->GetOutputHandler().SetTensorInfo(info); |
| 269 | |
| 270 | head = graph.InsertNewLayer<armnn::MemCopyLayer>(head->GetInputSlot(0), ""); |
| 271 | head->GetOutputHandler().SetTensorInfo(info); |
| 272 | |
| 273 | head = graph.InsertNewLayer<armnn::MultiplicationLayer>(head->GetInputSlot(0), ""); |
| 274 | head->GetOutputHandler().SetTensorInfo(info); |
| 275 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 276 | // Inserts input for 2nd input of Multiplication. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 277 | graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(1), inputId++, "") |
| 278 | ->GetOutputHandler().SetTensorInfo(info); |
| 279 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 280 | // Inserts input. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 281 | graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(0), inputId++, "") |
| 282 | ->GetOutputHandler().SetTensorInfo(info); |
| 283 | |
| 284 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 285 | graph.cend(), |
| 286 | &IsLayerOfType<armnn::InputLayer>, |
| 287 | &IsLayerOfType<armnn::InputLayer>, |
| 288 | &IsLayerOfType<armnn::InputLayer>, |
| 289 | &IsLayerOfType<armnn::MultiplicationLayer>, |
| 290 | &IsLayerOfType<armnn::MemCopyLayer>, |
| 291 | &IsLayerOfType<armnn::FloorLayer>, |
| 292 | &IsLayerOfType<armnn::FakeQuantizationLayer>, |
| 293 | &IsLayerOfType<armnn::AdditionLayer>, |
| 294 | &IsLayerOfType<armnn::ActivationLayer>, |
| 295 | &IsLayerOfType<armnn::PermuteLayer>, |
| 296 | &IsLayerOfType<armnn::OutputLayer>)); |
| 297 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 298 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(MovePermuteUp())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 299 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 300 | // The permute is moved to the top. New permutes for layers with multiple inputs. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 301 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 302 | graph.cend(), |
| 303 | &IsLayerOfType<armnn::InputLayer>, |
| 304 | &IsLayerOfType<armnn::InputLayer>, |
| 305 | &IsLayerOfType<armnn::InputLayer>, |
| 306 | &IsLayerOfType<armnn::PermuteLayer>, |
| 307 | &IsLayerOfType<armnn::PermuteLayer>, |
| 308 | &IsLayerOfType<armnn::PermuteLayer>, |
| 309 | &IsLayerOfType<armnn::MultiplicationLayer>, |
| 310 | &IsLayerOfType<armnn::MemCopyLayer>, |
| 311 | &IsLayerOfType<armnn::FloorLayer>, |
| 312 | &IsLayerOfType<armnn::FakeQuantizationLayer>, |
| 313 | &IsLayerOfType<armnn::AdditionLayer>, |
| 314 | &IsLayerOfType<armnn::ActivationLayer>, |
| 315 | &IsLayerOfType<armnn::OutputLayer>)); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 316 | |
| 317 | std::list<std::string> testRelatedLayers = { permuteLayerName }; |
| 318 | |
| 319 | BOOST_TEST(CheckRelatedLayers<armnn::PermuteLayer>(graph, testRelatedLayers)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 320 | } |
| 321 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 322 | BOOST_AUTO_TEST_CASE(PermuteAsReshapeTest) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 323 | { |
| 324 | armnn::Graph graph; |
| 325 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 326 | std::string permuteLayerName = "permute"; |
| 327 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 328 | const armnn::TensorInfo infoIn({ 1, 2, 3, 1 }, armnn::DataType::Float32); |
| 329 | const armnn::TensorInfo infoOut({ 1, 1, 2, 3 }, armnn::DataType::Float32); |
| 330 | |
| 331 | auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); |
| 332 | |
| 333 | graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input") |
| 334 | ->GetOutputHandler().SetTensorInfo(infoIn); |
| 335 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 336 | // Inserts permute. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 337 | graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 338 | armnn::PermuteDescriptor({ 0, 2, 3, 1 }), permuteLayerName.c_str()) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 339 | ->GetOutputHandler().SetTensorInfo(infoOut); |
| 340 | |
| 341 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 342 | graph.cend(), |
| 343 | &IsLayerOfType<armnn::InputLayer>, |
| 344 | &IsLayerOfType<armnn::PermuteLayer>, |
| 345 | &IsLayerOfType<armnn::OutputLayer>)); |
| 346 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 347 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(PermuteAsReshape())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 348 | |
| 349 | // The permute is replaced by an equivalent reshape. |
| 350 | |
| 351 | auto checkReshape = [&infoOut](const armnn::Layer* const layer) -> bool |
| 352 | { |
| 353 | const auto reshapeLayer = static_cast<const armnn::ReshapeLayer*>(layer); |
| 354 | return IsLayerOfType<armnn::ReshapeLayer>(layer) && |
| 355 | (reshapeLayer->GetParameters().m_TargetShape == infoOut.GetShape()) && |
| 356 | (reshapeLayer->GetOutputHandler().GetTensorInfo().GetShape() == infoOut.GetShape()); |
| 357 | }; |
| 358 | |
| 359 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 360 | graph.cend(), |
| 361 | &IsLayerOfType<armnn::InputLayer>, |
| 362 | checkReshape, |
| 363 | &IsLayerOfType<armnn::OutputLayer>)); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 364 | |
| 365 | |
| 366 | std::list<std::string> testRelatedLayers = { permuteLayerName }; |
| 367 | BOOST_TEST(CheckRelatedLayers<armnn::ReshapeLayer>(graph, testRelatedLayers)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 368 | } |
| 369 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 370 | BOOST_AUTO_TEST_CASE(OptimizeConsecutiveReshapesTest) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 371 | { |
| 372 | armnn::Graph graph; |
| 373 | |
| 374 | const armnn::TensorInfo info0({ 1, 2, 3, 5 }, armnn::DataType::Float32); |
| 375 | |
| 376 | auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); |
| 377 | auto input = graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input"); |
| 378 | |
| 379 | input->GetOutputHandler().SetTensorInfo(info0); |
| 380 | |
| 381 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 382 | // Inserts two reshapes. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 383 | const armnn::TensorInfo info1({1, 30, 1, 1}, armnn::DataType::Float32); |
| 384 | const armnn::TensorInfo info2({1, 2, 1, 15}, armnn::DataType::Float32); |
| 385 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 386 | std::string reshape1Name = "reshape1"; |
| 387 | std::string reshape2Name = "reshape2"; |
| 388 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 389 | auto reshape1 = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), |
| 390 | armnn::ReshapeDescriptor{ info1.GetShape() }, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 391 | reshape1Name.c_str()); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 392 | auto reshape2 = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), |
| 393 | armnn::ReshapeDescriptor{ info2.GetShape() }, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 394 | reshape2Name.c_str()); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 395 | |
| 396 | reshape1->GetOutputHandler().SetTensorInfo(info1); |
| 397 | reshape2->GetOutputHandler().SetTensorInfo(info2); |
| 398 | |
| 399 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 400 | graph.cend(), |
| 401 | &IsLayerOfType<armnn::InputLayer>, |
| 402 | &IsLayerOfType<armnn::ReshapeLayer>, |
| 403 | &IsLayerOfType<armnn::ReshapeLayer>, |
| 404 | &IsLayerOfType<armnn::OutputLayer>)); |
| 405 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 406 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(OptimizeConsecutiveReshapes())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 407 | |
| 408 | auto checkReshape = [&info2](const armnn::Layer* const layer) -> bool |
| 409 | { |
| 410 | const auto reshapeLayer = static_cast<const armnn::ReshapeLayer*>(layer); |
| 411 | return IsLayerOfType<armnn::ReshapeLayer>(layer) && |
| 412 | (reshapeLayer->GetParameters().m_TargetShape == info2.GetShape()) && |
| 413 | (reshapeLayer->GetOutputHandler().GetTensorInfo().GetShape() == info2.GetShape()); |
| 414 | }; |
| 415 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 416 | // The two reshapes are replaced by a single equivalent reshape. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 417 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 418 | graph.cend(), |
| 419 | &IsLayerOfType<armnn::InputLayer>, |
| 420 | checkReshape, |
| 421 | &IsLayerOfType<armnn::OutputLayer>)); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 422 | |
| 423 | // Check the new reshape layer has the other two reshapes as related layers |
| 424 | std::list<std::string> testRelatedLayers = { reshape2Name, reshape1Name }; |
| 425 | |
| 426 | BOOST_TEST(CheckRelatedLayers<armnn::ReshapeLayer>(graph, testRelatedLayers)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 427 | } |
| 428 | |
| 429 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 430 | // Inserts a reshape to the input shape. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 431 | auto reshapeToIn = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), |
| 432 | armnn::ReshapeDescriptor{ info0.GetShape() }, |
| 433 | "reshapeToIn"); |
| 434 | |
| 435 | reshapeToIn->GetOutputHandler().SetTensorInfo(info0); |
| 436 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 437 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(OptimizeConsecutiveReshapes())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 438 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 439 | // The two reshapes are removed. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 440 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 441 | graph.cend(), |
| 442 | &IsLayerOfType<armnn::InputLayer>, |
| 443 | &IsLayerOfType<armnn::OutputLayer>)); |
| 444 | } |
| 445 | } |
| 446 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 447 | BOOST_AUTO_TEST_CASE(SquashEqualSiblingsTest) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 448 | { |
| 449 | armnn::Graph graph; |
| 450 | |
| 451 | armnn::LayerBindingId outputId = 0; |
| 452 | |
| 453 | const armnn::TensorInfo info({ 1, 2, 3, 5 }, armnn::DataType::Float32); |
| 454 | const armnn::TensorInfo permuted({ 1, 5, 2, 3 }, armnn::DataType::Float32); |
| 455 | |
| 456 | auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); |
| 457 | input->GetOutputSlot().SetTensorInfo(info); |
| 458 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 459 | // Inserts equal permutes, equal reshapes and something else. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 460 | const armnn::PermuteDescriptor permDesc({ 0, 2, 3, 1 }); |
| 461 | const armnn::ReshapeDescriptor reshapeDesc{ { 1, 3, 1, 5 } }; |
| 462 | |
| 463 | armnn::Layer* layer; |
| 464 | |
| 465 | layer = graph.AddLayer<armnn::PermuteLayer>(permDesc, ""); |
| 466 | layer->GetOutputSlot().SetTensorInfo(permuted); |
| 467 | layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); |
| 468 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 469 | |
| 470 | layer = graph.AddLayer<armnn::ReshapeLayer>(reshapeDesc, ""); |
| 471 | layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); |
| 472 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 473 | |
| 474 | layer = graph.AddLayer<armnn::FloorLayer>(""); |
| 475 | layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); |
| 476 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 477 | |
| 478 | layer = graph.AddLayer<armnn::ReshapeLayer>(reshapeDesc, ""); |
| 479 | layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); |
| 480 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 481 | |
| 482 | layer = graph.AddLayer<armnn::PermuteLayer>(permDesc, ""); |
| 483 | layer->GetOutputSlot().SetTensorInfo(permuted); |
| 484 | layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); |
| 485 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 486 | |
| 487 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 488 | graph.cend(), |
| 489 | &IsLayerOfType<armnn::InputLayer>, |
| 490 | &IsLayerOfType<armnn::PermuteLayer>, |
| 491 | &IsLayerOfType<armnn::ReshapeLayer>, |
| 492 | &IsLayerOfType<armnn::FloorLayer>, |
| 493 | &IsLayerOfType<armnn::ReshapeLayer>, |
| 494 | &IsLayerOfType<armnn::PermuteLayer>, |
| 495 | &IsLayerOfType<armnn::OutputLayer>, |
| 496 | &IsLayerOfType<armnn::OutputLayer>, |
| 497 | &IsLayerOfType<armnn::OutputLayer>, |
| 498 | &IsLayerOfType<armnn::OutputLayer>, |
| 499 | &IsLayerOfType<armnn::OutputLayer>)); |
| 500 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 501 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(SquashEqualPermuteSiblings(), |
| 502 | SquashEqualReshapeSiblings())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 503 | |
| 504 | // The permutes and reshapes are squashed. |
| 505 | |
| 506 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 507 | graph.cend(), |
| 508 | &IsLayerOfType<armnn::InputLayer>, |
| 509 | &IsLayerOfType<armnn::PermuteLayer>, |
| 510 | &IsLayerOfType<armnn::ReshapeLayer>, |
| 511 | &IsLayerOfType<armnn::FloorLayer>, |
| 512 | &IsLayerOfType<armnn::OutputLayer>, |
| 513 | &IsLayerOfType<armnn::OutputLayer>, |
| 514 | &IsLayerOfType<armnn::OutputLayer>, |
| 515 | &IsLayerOfType<armnn::OutputLayer>, |
| 516 | &IsLayerOfType<armnn::OutputLayer>)); |
| 517 | } |
| 518 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 519 | BOOST_AUTO_TEST_CASE(ConvertConstantsHalfToFloatTest) |
| 520 | { |
| 521 | armnn::Graph graph; |
| 522 | |
| 523 | const armnn::TensorInfo info({ 1,1,1,2 }, armnn::DataType::Float32); |
| 524 | |
| 525 | // Create the half precision input data |
| 526 | unsigned int dims[] = { 4,1,1,1 }; |
| 527 | std::vector<float> convWeightsData{1.f, 2.f, 3.f, 4.f}; |
| 528 | std::vector<uint16_t> halfWeights(4); |
| 529 | armnnUtils::FloatingPointConverter::ConvertFloat32To16(convWeightsData.data(), |
| 530 | convWeightsData.size(), |
| 531 | halfWeights.data()); |
| 532 | armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float16), halfWeights); |
| 533 | |
| 534 | //Create the simple test network |
| 535 | auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); |
| 536 | input->GetOutputSlot().SetTensorInfo(info); |
| 537 | |
| 538 | auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc"); |
| 539 | fc->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); |
| 540 | fc->GetOutputSlot().SetTensorInfo(info); |
| 541 | |
| 542 | auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); |
| 543 | |
| 544 | //Connect up the layers |
| 545 | input->GetOutputSlot().Connect(fc->GetInputSlot(0)); |
| 546 | fc->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| 547 | |
| 548 | //Test the tensor info is correct. |
| 549 | BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16); |
| 550 | |
| 551 | // Run the optimizer |
| 552 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsHalfToFloat())); |
| 553 | |
| 554 | //Test the tensor info is correct. |
| 555 | BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); |
| 556 | |
| 557 | // Now test the data matches float32 data |
| 558 | float* data = fc->m_Weight->GetTensor<float>(); |
| 559 | BOOST_CHECK(1.0f == data[0]); |
| 560 | BOOST_CHECK(2.0f == data[1]); |
| 561 | BOOST_CHECK(3.0f == data[2]); |
| 562 | BOOST_CHECK(4.0f == data[3]); |
| 563 | } |
| 564 | |
| 565 | BOOST_AUTO_TEST_CASE(ConvertConstantsFloatToHalfTest) |
| 566 | { |
| 567 | armnn::Graph graph; |
| 568 | |
| 569 | const armnn::TensorInfo info({ 1, 1, 1, 2 }, armnn::DataType::Float16); |
| 570 | |
| 571 | // Create const tensor from fp32 data |
| 572 | unsigned int dims[] = { 4, 1, 1, 1 }; |
| 573 | std::vector<float> floatWeights{ 1.0f, 2.0f, 3.0f, 4.0f }; |
| 574 | armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32), floatWeights); |
| 575 | |
| 576 | // Create simple test network |
| 577 | auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); |
| 578 | input->GetOutputSlot().SetTensorInfo(info); |
| 579 | |
| 580 | auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc"); |
| 581 | fc->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); |
| 582 | fc->GetOutputSlot().SetTensorInfo(info); |
| 583 | |
| 584 | auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); |
| 585 | |
| 586 | // Connect up the layers |
| 587 | input->GetOutputSlot().Connect(fc->GetInputSlot(0)); |
| 588 | fc->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| 589 | |
| 590 | // Check tensor data type before conversion |
| 591 | BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); |
| 592 | |
| 593 | // Run the optimizer |
| 594 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf())); |
| 595 | |
| 596 | // Check tensor data type after conversion |
| 597 | BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16); |
| 598 | |
| 599 | // Check whether data matches expected fp16 data |
| 600 | Half* data = fc->m_Weight->GetTensor<Half>(); |
| 601 | BOOST_CHECK(data[0] == Half(1.0f)); |
| 602 | BOOST_CHECK(data[1] == Half(2.0f)); |
| 603 | BOOST_CHECK(data[2] == Half(3.0f)); |
| 604 | BOOST_CHECK(data[3] == Half(4.0f)); |
| 605 | } |
| 606 | |
| 607 | BOOST_AUTO_TEST_CASE(OptimizeInverseConversionsTest) |
| 608 | { |
| 609 | armnn::Graph graph; |
| 610 | |
| 611 | auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); |
| 612 | |
| 613 | graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input"); |
| 614 | |
| 615 | // Fp32ToFp16 conversion followed by an inverse Fp16ToFp32 conversion |
| 616 | graph.InsertNewLayer<armnn::ConvertFp32ToFp16Layer>(output->GetInputSlot(0), "convert1"); |
| 617 | graph.InsertNewLayer<armnn::ConvertFp16ToFp32Layer>(output->GetInputSlot(0), "convert2"); |
| 618 | |
| 619 | graph.InsertNewLayer<armnn::Convolution2dLayer>(output->GetInputSlot(0), Convolution2dDescriptor(), "conv"); |
| 620 | |
| 621 | // Fp16ToFp32 conversion followed by an inverse Fp32ToFp16 conversion |
| 622 | graph.InsertNewLayer<armnn::ConvertFp16ToFp32Layer>(output->GetInputSlot(0), "convert3"); |
| 623 | graph.InsertNewLayer<armnn::ConvertFp32ToFp16Layer>(output->GetInputSlot(0), "convert4"); |
| 624 | |
| 625 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 626 | graph.cend(), |
| 627 | &IsLayerOfType<armnn::InputLayer>, |
| 628 | &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, |
| 629 | &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, |
| 630 | &IsLayerOfType<armnn::Convolution2dLayer>, |
| 631 | &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, |
| 632 | &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, |
| 633 | &IsLayerOfType<armnn::OutputLayer>)); |
| 634 | |
| 635 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(OptimizeInverseConversionsFp16(), |
| 636 | OptimizeInverseConversionsFp32())); |
| 637 | |
| 638 | // Check that all consecutive inverse conversions are removed |
| 639 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 640 | graph.cend(), |
| 641 | &IsLayerOfType<armnn::InputLayer>, |
| 642 | &IsLayerOfType<armnn::Convolution2dLayer>, |
| 643 | &IsLayerOfType<armnn::OutputLayer>)); |
| 644 | } |
| 645 | |
| 646 | BOOST_AUTO_TEST_CASE(InsertConvertersTest) |
| 647 | { |
| 648 | const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float16); |
| 649 | |
| 650 | armnn::Graph graph; |
| 651 | |
| 652 | armnn::LayerBindingId inputId = 0; |
| 653 | |
| 654 | armnn::Layer* head = graph.AddLayer<armnn::OutputLayer>(0, "output"); |
| 655 | |
| 656 | head = graph.InsertNewLayer<armnn::AdditionLayer>(head->GetInputSlot(0), ""); |
| 657 | head->GetOutputHandler().SetTensorInfo(info); |
| 658 | |
| 659 | graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(1), inputId++, "") |
| 660 | ->GetOutputHandler().SetTensorInfo(info); |
| 661 | |
| 662 | head = graph.InsertNewLayer<armnn::FloorLayer>(head->GetInputSlot(0), ""); |
| 663 | head->GetOutputHandler().SetTensorInfo(info); |
| 664 | |
| 665 | head = graph.InsertNewLayer<armnn::MemCopyLayer>(head->GetInputSlot(0), ""); |
| 666 | head->GetOutputHandler().SetTensorInfo(info); |
| 667 | |
| 668 | graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(0), inputId++, "") |
| 669 | ->GetOutputHandler().SetTensorInfo(info); |
| 670 | |
| 671 | // Check graph layer sequence before inserting convert layers |
| 672 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 673 | graph.cend(), |
| 674 | &IsLayerOfType<armnn::InputLayer>, |
| 675 | &IsLayerOfType<armnn::InputLayer>, |
| 676 | &IsLayerOfType<armnn::MemCopyLayer>, |
| 677 | &IsLayerOfType<armnn::FloorLayer>, |
| 678 | &IsLayerOfType<armnn::AdditionLayer>, |
| 679 | &IsLayerOfType<armnn::OutputLayer>)); |
| 680 | |
| 681 | // Check layers have Float16 DataType |
| 682 | for (auto& layer : graph) |
| 683 | { |
| 684 | if(layer->GetType()==LayerType::Floor || layer->GetType() == LayerType::Addition) |
| 685 | { |
| 686 | BOOST_ASSERT(layer->GetOutputSlot(0).GetTensorInfo().GetDataType() == DataType::Float16); |
| 687 | BOOST_ASSERT(layer->GetDataType() == DataType::Float16); |
| 688 | } |
| 689 | } |
| 690 | |
| 691 | // Insert convert layers either side of unsupported layer |
| 692 | for (auto& layer : graph) |
| 693 | { |
| 694 | if(layer->GetType()==LayerType::Floor || layer->GetType() == LayerType::Addition) |
| 695 | { |
| 696 | InsertConvertFp16ToFp32LayersBefore(graph, *layer); |
| 697 | InsertConvertFp32ToFp16LayersAfter(graph, *layer); |
| 698 | } |
| 699 | } |
| 700 | |
| 701 | // Check layers have correct DataType after inserting convert layers |
| 702 | for (auto& layer : graph) |
| 703 | { |
| 704 | if (layer->GetType()==LayerType::Floor || layer->GetType() == LayerType::Addition) |
| 705 | { |
| 706 | BOOST_ASSERT(layer->GetOutputSlot(0).GetTensorInfo().GetDataType() == DataType::Float32); |
| 707 | BOOST_ASSERT(layer->GetDataType() == DataType::Float32); |
| 708 | } |
| 709 | else if (layer->GetType() == LayerType::ConvertFp16ToFp32) |
| 710 | { |
| 711 | BOOST_ASSERT(layer->GetOutputSlot(0).GetTensorInfo().GetDataType() == DataType::Float32); |
| 712 | BOOST_ASSERT(layer->GetDataType() == DataType::Float16); |
| 713 | } |
| 714 | else if (layer->GetType() == LayerType::ConvertFp32ToFp16) |
| 715 | { |
| 716 | BOOST_ASSERT(layer->GetOutputSlot(0).GetTensorInfo().GetDataType() == DataType::Float16); |
| 717 | BOOST_ASSERT(layer->GetDataType() == DataType::Float32); |
| 718 | } |
| 719 | } |
| 720 | |
| 721 | // Check sequence of layers after inserting convert layers |
| 722 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 723 | graph.cend(), |
| 724 | &IsLayerOfType<armnn::InputLayer>, |
| 725 | &IsLayerOfType<armnn::InputLayer>, |
| 726 | &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, |
| 727 | &IsLayerOfType<armnn::MemCopyLayer>, |
| 728 | &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, |
| 729 | &IsLayerOfType<armnn::FloorLayer>, |
| 730 | &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, |
| 731 | &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, |
| 732 | &IsLayerOfType<armnn::AdditionLayer>, |
| 733 | &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, |
| 734 | &IsLayerOfType<armnn::OutputLayer>)); |
| 735 | } |
| 736 | |
| 737 | BOOST_AUTO_TEST_CASE(Fp32NetworkToFp16OptimizationTest) |
| 738 | { |
| 739 | armnn::Graph graph; |
| 740 | |
| 741 | const armnn::TensorInfo infoFP32({ 2,2,1,3 }, armnn::DataType::Float32); |
| 742 | |
| 743 | // Create the simple test network |
| 744 | auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); |
| 745 | input->GetOutputSlot().SetTensorInfo(infoFP32); |
| 746 | |
| 747 | auto floor = graph.AddLayer<armnn::FloorLayer>("floor"); |
| 748 | floor->GetOutputSlot().SetTensorInfo(infoFP32); |
| 749 | |
| 750 | auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); |
| 751 | |
| 752 | // Connect up the layers |
| 753 | input->GetOutputSlot().Connect(floor->GetInputSlot(0)); |
| 754 | floor->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| 755 | |
| 756 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 757 | graph.cend(), |
| 758 | &IsLayerOfType<armnn::InputLayer>, |
| 759 | &IsLayerOfType<armnn::FloorLayer>, |
| 760 | &IsLayerOfType<armnn::OutputLayer>)); |
| 761 | |
| 762 | // Run the optimizer |
| 763 | armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToFp16Converter())); |
| 764 | |
| 765 | BOOST_TEST(CheckSequence(graph.cbegin(), |
| 766 | graph.cend(), |
| 767 | &IsLayerOfType<armnn::InputLayer>, |
| 768 | &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, |
| 769 | &IsLayerOfType<armnn::FloorLayer>, |
| 770 | &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, |
| 771 | &IsLayerOfType<armnn::OutputLayer>)); |
| 772 | } |
| 773 | |
narpra01 | 7af7688 | 2018-10-26 17:36:32 +0100 | [diff] [blame^] | 774 | void CreateConvolution2dGraph(Graph &graph, const unsigned int* inputShape, |
| 775 | const unsigned int* weightsShape, const unsigned int* outputShape, |
| 776 | DataLayout dataLayout = DataLayout::NCHW) |
| 777 | { |
| 778 | armnn::TensorInfo inputInfo(4, inputShape, DataType::Float32); |
| 779 | armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32); |
| 780 | |
| 781 | std::vector<float> weightsVector(90); |
| 782 | armnn::ConstTensor weights(armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32), weightsVector); |
| 783 | |
| 784 | Convolution2dDescriptor desc; |
| 785 | desc.m_BiasEnabled = false; |
| 786 | desc.m_StrideX = 1; |
| 787 | desc.m_StrideY = 1; |
| 788 | desc.m_DataLayout = dataLayout; |
| 789 | |
| 790 | Layer* input = graph.AddLayer<InputLayer>(0, "input"); |
| 791 | input->GetOutputSlot().SetTensorInfo(inputInfo); |
| 792 | |
| 793 | Convolution2dLayer* layer = graph.AddLayer<Convolution2dLayer>(desc, "conv2d"); |
| 794 | layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); |
| 795 | layer->GetOutputSlot().SetTensorInfo(outputInfo); |
| 796 | |
| 797 | Layer* output = graph.AddLayer<OutputLayer>(0, "output"); |
| 798 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 799 | layer->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| 800 | } |
| 801 | |
| 802 | BOOST_AUTO_TEST_CASE(Conv2dValidateTensorShapesFromInputs) |
| 803 | { |
| 804 | Graph graph; |
| 805 | const unsigned int inputShape[] = { 1, 3, 8, 16 }; |
| 806 | const unsigned int weightsShape[] = { 2, 3, 5, 3 }; |
| 807 | const unsigned int outputShape[] = { 1, 2, 4, 14 }; |
| 808 | CreateConvolution2dGraph(graph, inputShape, weightsShape, outputShape); |
| 809 | |
| 810 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 811 | } |
| 812 | |
| 813 | BOOST_AUTO_TEST_CASE(Conv2dValidateTensorShapesFromInputsNhwc) |
| 814 | { |
| 815 | Graph graph; |
| 816 | const unsigned int inputShape[] = { 1, 8, 16, 3 }; |
| 817 | const unsigned int weightsShape[] = { 2, 5, 3, 3 }; |
| 818 | const unsigned int outputShape[] = { 1, 4, 14, 2 }; |
| 819 | CreateConvolution2dGraph(graph, inputShape, weightsShape, outputShape, DataLayout::NHWC); |
| 820 | |
| 821 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 822 | } |
| 823 | |
| 824 | void CreateDepthwiseConvolution2dGraph(Graph &graph, const unsigned int* inputShape, |
| 825 | const unsigned int* weightsShape, const unsigned int* outputShape, |
| 826 | DataLayout dataLayout = DataLayout::NCHW) |
| 827 | { |
| 828 | armnn::TensorInfo inputInfo(4, inputShape, DataType::Float32); |
| 829 | armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32); |
| 830 | |
| 831 | std::vector<float> weightsVector(18); |
| 832 | armnn::ConstTensor weights(armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32), weightsVector); |
| 833 | |
| 834 | DepthwiseConvolution2dDescriptor desc; |
| 835 | desc.m_BiasEnabled = false; |
| 836 | desc.m_StrideX = 1; |
| 837 | desc.m_StrideY = 1; |
| 838 | desc.m_DataLayout = dataLayout; |
| 839 | |
| 840 | Layer* input = graph.AddLayer<InputLayer>(0, "input"); |
| 841 | input->GetOutputSlot().SetTensorInfo(inputInfo); |
| 842 | |
| 843 | DepthwiseConvolution2dLayer* layer = graph.AddLayer<DepthwiseConvolution2dLayer>(desc, "depthwiseConv2d"); |
| 844 | layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); |
| 845 | layer->GetOutputSlot().SetTensorInfo(outputInfo); |
| 846 | |
| 847 | Layer* output = graph.AddLayer<OutputLayer>(0, "output"); |
| 848 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 849 | layer->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| 850 | } |
| 851 | |
| 852 | BOOST_AUTO_TEST_CASE(DepthwiseConv2dValidateTensorShapesFromInputs) |
| 853 | { |
| 854 | Graph graph; |
| 855 | const unsigned int inputShape[] = { 1, 2, 3, 3 }; |
| 856 | const unsigned int weightsShape[] = { 1, 2, 3, 3 }; |
| 857 | const unsigned int outputShape[] = { 1, 2, 1, 1 }; |
| 858 | CreateDepthwiseConvolution2dGraph(graph, inputShape, weightsShape, outputShape); |
| 859 | |
| 860 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 861 | } |
| 862 | |
| 863 | BOOST_AUTO_TEST_CASE(DepthwiseConv2dValidateTensorShapesFromInputsNhwc) |
| 864 | { |
| 865 | Graph graph; |
| 866 | const unsigned int inputShape[] = { 1, 3, 3, 2 }; |
| 867 | const unsigned int weightsShape[] = { 1, 3, 3, 2 }; |
| 868 | const unsigned int outputShape[] = { 1, 1, 1, 2 }; |
| 869 | CreateDepthwiseConvolution2dGraph(graph, inputShape, weightsShape, outputShape, DataLayout::NHWC); |
| 870 | |
| 871 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 872 | } |
| 873 | |
| 874 | void CreatePooling2dGraph(Graph &graph, const unsigned int* inputShape, const unsigned int* outputShape, |
| 875 | DataLayout dataLayout = DataLayout::NCHW) |
| 876 | { |
| 877 | armnn::TensorInfo inputInfo(4, inputShape, DataType::Float32); |
| 878 | armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32); |
| 879 | |
| 880 | Pooling2dDescriptor desc; |
| 881 | desc.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 882 | desc.m_PoolWidth = desc.m_PoolHeight = 100; |
| 883 | desc.m_StrideX = desc.m_StrideY = 5; |
| 884 | desc.m_PadLeft = 50; |
| 885 | desc.m_PadRight = 50; |
| 886 | desc.m_PadTop = 50; |
| 887 | desc.m_PadBottom = 50; |
| 888 | desc.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 889 | desc.m_DataLayout = dataLayout; |
| 890 | |
| 891 | Layer* input = graph.AddLayer<InputLayer>(0, "input"); |
| 892 | input->GetOutputSlot().SetTensorInfo(inputInfo); |
| 893 | |
| 894 | Pooling2dLayer* layer = graph.AddLayer<Pooling2dLayer>(desc, "pooling2d"); |
| 895 | layer->GetOutputSlot().SetTensorInfo(outputInfo); |
| 896 | |
| 897 | Layer* output = graph.AddLayer<OutputLayer>(0, "output"); |
| 898 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 899 | layer->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| 900 | } |
| 901 | |
| 902 | BOOST_AUTO_TEST_CASE(Pooling2dValidateTensorShapesFromInputs) |
| 903 | { |
| 904 | Graph graph; |
| 905 | const unsigned int inputShape[] = { 5, 3, 52, 60 }; |
| 906 | const unsigned int outputShape[] = { 5, 3, 11, 13 }; |
| 907 | CreatePooling2dGraph(graph, inputShape, outputShape, DataLayout::NCHW); |
| 908 | |
| 909 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 910 | } |
| 911 | |
| 912 | BOOST_AUTO_TEST_CASE(Pooling2dValidateTensorShapesFromInputsNhwc) |
| 913 | { |
| 914 | Graph graph; |
| 915 | const unsigned int inputShape[] = { 5, 52, 60, 3 }; |
| 916 | const unsigned int outputShape[] = { 5, 11, 13, 3 }; |
| 917 | CreatePooling2dGraph(graph, inputShape, outputShape, DataLayout::NHWC); |
| 918 | |
| 919 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 920 | } |
| 921 | |
| 922 | void CreateResizeBilinearGraph(Graph &graph, const unsigned int* inputShape, const unsigned int* outputShape, |
| 923 | DataLayout dataLayout = DataLayout::NCHW) |
| 924 | { |
| 925 | armnn::TensorInfo inputInfo(4, inputShape, DataType::Float32); |
| 926 | armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32); |
| 927 | |
| 928 | ResizeBilinearDescriptor desc; |
| 929 | desc.m_TargetHeight = 3; |
| 930 | desc.m_TargetWidth = 4; |
| 931 | desc.m_DataLayout = dataLayout; |
| 932 | |
| 933 | Layer* input = graph.AddLayer<InputLayer>(0, "input"); |
| 934 | input->GetOutputSlot().SetTensorInfo(inputInfo); |
| 935 | |
| 936 | ResizeBilinearLayer* layer = graph.AddLayer<ResizeBilinearLayer>(desc, "resizeBilinear"); |
| 937 | layer->GetOutputSlot().SetTensorInfo(outputInfo); |
| 938 | |
| 939 | Layer* output = graph.AddLayer<OutputLayer>(0, "output"); |
| 940 | input->GetOutputSlot().Connect(layer->GetInputSlot(0)); |
| 941 | layer->GetOutputSlot().Connect(output->GetInputSlot(0)); |
| 942 | } |
| 943 | |
| 944 | BOOST_AUTO_TEST_CASE(ResizeBilinearValidateTensorShapesFromInputs) |
| 945 | { |
| 946 | Graph graph; |
| 947 | const unsigned int inputShape[] = { 1, 2, 4, 5 }; |
| 948 | const unsigned int outputShape[] = { 1, 2, 3, 4 }; |
| 949 | CreateResizeBilinearGraph(graph, inputShape, outputShape); |
| 950 | |
| 951 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 952 | } |
| 953 | |
| 954 | BOOST_AUTO_TEST_CASE(ResizeBilinearValidateTensorShapesFromInputsNhwc) |
| 955 | { |
| 956 | Graph graph; |
| 957 | const unsigned int inputShape[] = { 1, 4, 5, 2 }; |
| 958 | const unsigned int outputShape[] = { 1, 3, 4, 2 }; |
| 959 | CreateResizeBilinearGraph(graph, inputShape, outputShape, DataLayout::NHWC); |
| 960 | |
| 961 | BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); |
| 962 | } |
| 963 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 964 | BOOST_AUTO_TEST_SUITE_END() |