ArmNN
 24.02
CreateWorkload.hpp
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1 //
2 // Copyright © 2017,2021-2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 #pragma once
6 
7 #include "TestUtils.hpp"
8 
9 #include <Graph.hpp>
10 #include <Network.hpp>
11 #include <ResolveType.hpp>
12 
13 #include <armnnUtils/DataLayoutIndexed.hpp>
14 #include <armnn/backends/TensorHandle.hpp>
15 #include <armnn/backends/WorkloadData.hpp>
16 #include <armnn/backends/WorkloadFactory.hpp>
17 #include <armnn/utility/Assert.hpp>
18 #include <armnn/utility/IgnoreUnused.hpp>
19 #include <armnn/utility/PolymorphicDowncast.hpp>
20 
21 #include <doctest/doctest.h>
22 
23 #include <utility>
24 
25 using namespace armnn;
26 
27 namespace
28 {
29 
30 using namespace std;
31 
32 // Calls CreateWorkload for a layer, and checks the returned pointer is of the correct type.
33 template<typename Workload>
34 std::unique_ptr<Workload> MakeAndCheckWorkload(Layer& layer,
35  const IWorkloadFactory& factory,
36  const ModelOptions& modelOptions = {})
37 {
38  std::unique_ptr<IWorkload> workload = layer.CreateWorkload(factory);
39  CHECK_MESSAGE(workload.get() == PolymorphicDowncast<Workload*>(workload.get()),
40  "Cannot convert to derived class");
41  std::string reasonIfUnsupported;
42  layer.SetBackendId(factory.GetBackendId());
43  CHECK(factory.IsLayerSupported(layer, layer.GetDataType(), reasonIfUnsupported, modelOptions));
44  return std::unique_ptr<Workload>(static_cast<Workload*>(workload.release()));
45 }
46 
47 // Helper function to create tensor handlers for workloads, assuming they all use the same factory.
48 void CreateTensorHandles(armnn::Graph& graph,
49  armnn::IWorkloadFactory& factory)
50 {
51  TensorHandleFactoryRegistry tmpRegistry;
52  for (auto&& layer : graph.TopologicalSort())
53  {
54  layer->CreateTensorHandles(tmpRegistry, factory);
55  }
56 }
57 
58 /////////////////////////////////////////////////////////////////////////////////////////////
59 // The following functions are called by backendsCommon/test/CreateWorkload*.cpp
60 // They build very simple graphs, and then create a workload.
61 // Some checks are performed on the workload to ensure parameters have been passed correctly.
62 // They return the created workloads so that backend-specific checks can be performed.
63 /////////////////////////////////////////////////////////////////////////////////////////////
64 
65 template <typename ActivationWorkload, armnn::DataType DataType>
66 std::unique_ptr<ActivationWorkload> CreateActivationWorkloadTest(armnn::IWorkloadFactory& factory,
67  armnn::Graph& graph)
68 {
69  // Creates the layer we're testing.
70  ActivationDescriptor layerDesc;
71  layerDesc.m_Function = ActivationFunction::ReLu;
72  layerDesc.m_A = 3.5f;
73  layerDesc.m_B = -10.0f;
74 
75  ActivationLayer* const layer = graph.AddLayer<ActivationLayer>(layerDesc, "layer");
76 
77  // Creates extra layers.
78  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
79  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
80 
81  // Connects up.
82  armnn::TensorInfo tensorInfo({1, 1}, DataType);
83 
84  Connect(input, layer, tensorInfo);
85  Connect(layer, output, tensorInfo);
86 
87  CreateTensorHandles(graph, factory);
88 
89  // Makes the workload and checks it.
90  auto workload = MakeAndCheckWorkload<ActivationWorkload>(*layer, factory);
91 
92  ActivationQueueDescriptor queueDescriptor = workload->GetData();
93  CHECK(queueDescriptor.m_Inputs.size() == 1);
94  CHECK(queueDescriptor.m_Outputs.size() == 1);
95  CHECK(queueDescriptor.m_Parameters.m_A == 3.5f);
96  CHECK(queueDescriptor.m_Parameters.m_B == -10.0f);
97  CHECK((queueDescriptor.m_Parameters.m_Function == ActivationFunction::ReLu));
98 
99  // Returns so we can do extra, backend-specific tests.
100  return workload;
101 }
102 
103 template <typename WorkloadType,
104  typename DescriptorType,
105  typename LayerType,
106  armnn::DataType DataType>
107 std::unique_ptr<WorkloadType> CreateElementwiseWorkloadTest(armnn::IWorkloadFactory & factory,
108  armnn::Graph & graph)
109 {
110  // Creates the layer we're testing.
111  Layer* const layer = graph.AddLayer<LayerType>("layer");
112 
113  // Creates extra layers.
114  Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1");
115  Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2");
116  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
117 
118  // Connects up.
119  armnn::TensorInfo tensorInfo({2, 3}, DataType);
120  Connect(input1, layer, tensorInfo, 0, 0);
121  Connect(input2, layer, tensorInfo, 0, 1);
122  Connect(layer, output, tensorInfo);
123  CreateTensorHandles(graph, factory);
124 
125  // Makes the workload and checks it.
126  auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);
127 
128  auto queueDescriptor = workload->GetData();
129  CHECK(queueDescriptor.m_Inputs.size() == 2);
130  CHECK(queueDescriptor.m_Outputs.size() == 1);
131 
132  // Returns so we can do extra, backend-specific tests.
133  return workload;
134 }
135 
136 template <typename WorkloadType, armnn::DataType DataType>
137 std::unique_ptr<WorkloadType> CreateElementwiseBinaryWorkloadTest(armnn::IWorkloadFactory & factory,
138  armnn::Graph & graph,
139  armnn::BinaryOperation binaryOperation)
140 {
141  // Creates the layer we're testing.
142  ElementwiseBinaryDescriptor descriptor(binaryOperation);
143  //ElementwiseBinaryDescriptor descriptor = ElementwiseBinaryDescriptor(binaryOperation);
144 
145  Layer* const layer = graph.AddLayer<ElementwiseBinaryLayer>(descriptor, "layer");
146 
147  // Creates extra layers.
148  Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1");
149  Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2");
150  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
151 
152  // Connects up.
153  armnn::TensorInfo tensorInfo({2, 3}, DataType);
154  Connect(input1, layer, tensorInfo, 0, 0);
155  Connect(input2, layer, tensorInfo, 0, 1);
156  Connect(layer, output, tensorInfo);
157  CreateTensorHandles(graph, factory);
158 
159  // Makes the workload and checks it.
160  auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);
161 
162  auto queueDescriptor = workload->GetData();
163  CHECK(queueDescriptor.m_Inputs.size() == 2);
164  CHECK(queueDescriptor.m_Outputs.size() == 1);
165 
166  // Returns so we can do extra, backend-specific tests.
167  return workload;
168 }
169 
170 template<typename WorkloadType,
171  typename DescriptorType,
172  armnn::DataType DataType>
173 std::unique_ptr<WorkloadType> CreateSubtractionWithBlobWorkloadTest(armnn::IWorkloadFactory& factory,
174  armnn::Graph& graph)
175 {
176  // Creates the layer we're testing.
177  SubtractionLayer* const layer = graph.AddLayer<SubtractionLayer>("layer");
178 
179  auto activationDesc = std::make_shared<ActivationDescriptor>();
180  activationDesc->m_A = 10.0f;
181  activationDesc->m_B = 5.0f;
182  activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu;
183 
184  layer->SetAdditionalInfoForObject(activationDesc);
185 
186  // Creates extra layers.
187  Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1");
188  Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2");
189  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
190 
191  // Connects up.
192  armnn::TensorInfo tensorInfo({2, 3}, DataType);
193  Connect(input1, layer, tensorInfo, 0, 0);
194  Connect(input2, layer, tensorInfo, 0, 1);
195  Connect(layer, output, tensorInfo);
196  CreateTensorHandles(graph, factory);
197 
198  // Check that the additional information can be queried from the layer
199  std::shared_ptr<ActivationDescriptor>
200  activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>();
201 
202  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f);
203  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f);
204  ARMNN_ASSERT(
205  static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
206  );
207 
208  // Makes the workload and checks it.
209  auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);
210 
211  DescriptorType queueDescriptor = workload->GetData();
212 
213  const ActivationDescriptor* queueDescBlobPtr =
214  queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>();
215  IgnoreUnused(queueDescBlobPtr);
216  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f);
217  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f);
218  ARMNN_ASSERT(
219  static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
220  );
221 
222  CHECK(queueDescriptor.m_Inputs.size() == 2);
223  CHECK(queueDescriptor.m_Outputs.size() == 1);
224 
225  return workload;
226 }
227 
228 
229 template<typename WorkloadType,
230  typename DescriptorType,
231  armnn::DataType DataType>
232 std::unique_ptr<WorkloadType> CreateMultiplicationWithBlobWorkloadTest(armnn::IWorkloadFactory& factory,
233  armnn::Graph& graph)
234 {
235  // Creates the layer we're testing.
236  MultiplicationLayer* const layer = graph.AddLayer<MultiplicationLayer>("layer");
237 
238  auto activationDesc = std::make_shared<ActivationDescriptor>();
239  activationDesc->m_A = 10.0f;
240  activationDesc->m_B = 5.0f;
241  activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu;
242 
243  layer->SetAdditionalInfoForObject(activationDesc);
244 
245  // Creates extra layers.
246  Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1");
247  Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2");
248  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
249 
250  // Connects up.
251  armnn::TensorInfo tensorInfo({2, 3}, DataType);
252  Connect(input1, layer, tensorInfo, 0, 0);
253  Connect(input2, layer, tensorInfo, 0, 1);
254  Connect(layer, output, tensorInfo);
255  CreateTensorHandles(graph, factory);
256 
257  // Check that the additional information can be queried from the layer
258  std::shared_ptr<ActivationDescriptor>
259  activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>();
260 
261  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f);
262  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f);
263  ARMNN_ASSERT(
264  static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
265  );
266 
267  // Makes the workload and checks it.
268  auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);
269 
270  DescriptorType queueDescriptor = workload->GetData();
271  CHECK(queueDescriptor.m_Inputs.size() == 2);
272  CHECK(queueDescriptor.m_Outputs.size() == 1);
273  const ActivationDescriptor* queueDescBlobPtr =
274  queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>();
275  IgnoreUnused(queueDescBlobPtr);
276  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f);
277  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f);
278  ARMNN_ASSERT(
279  static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
280  );
281 
282  return workload;// Returns so we can do extra, backend-specific tests.
283 }
284 
285 template<typename WorkloadType,
286  typename DescriptorType,
287  armnn::DataType DataType>
288 std::unique_ptr<WorkloadType> CreateAdditionWithBlobWorkloadTest(armnn::IWorkloadFactory& factory,
289  armnn::Graph& graph)
290 {
291  // Creates the layer we're testing.
292  AdditionLayer* const layer = graph.AddLayer<AdditionLayer>("layer");
293 
294  auto activationDesc = std::make_shared<ActivationDescriptor>();
295  activationDesc->m_A = 10.0f;
296  activationDesc->m_B = 5.0f;
297  activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu;
298 
299  layer->SetAdditionalInfoForObject(activationDesc);
300 
301  // Creates extra layers.
302  Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1");
303  Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2");
304  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
305 
306  // Connects up.
307  armnn::TensorInfo tensorInfo({2, 3}, DataType);
308  Connect(input1, layer, tensorInfo, 0, 0);
309  Connect(input2, layer, tensorInfo, 0, 1);
310  Connect(layer, output, tensorInfo);
311  CreateTensorHandles(graph, factory);
312 
313  // Check that the additional information can be queried from the layer
314  std::shared_ptr<ActivationDescriptor>
315  activationDescPtr = layer->template GetAdditionalInformation<ActivationDescriptor>();
316 
317  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f);
318  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f);
319  ARMNN_ASSERT(
320  static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
321  );
322 
323  // Makes the workload and checks it.
324  auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);
325 
326  DescriptorType queueDescriptor = workload->GetData();
327  const ActivationDescriptor* queueDescBlobPtr =
328  queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>();
329  IgnoreUnused(queueDescBlobPtr);
330  CHECK(queueDescriptor.m_Inputs.size() == 2);
331  CHECK(queueDescriptor.m_Outputs.size() == 1);
332  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f);
333  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f);
334  ARMNN_ASSERT(
335  static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
336  );
337 
338  return workload;
339 }
340 
341 template <typename WorkloadType,
342  typename DescriptorType,
343  armnn::DataType DataType>
344 std::unique_ptr<WorkloadType> CreateElementwiseUnaryWorkloadTest(armnn::IWorkloadFactory & factory,
345  armnn::Graph & graph,
346  armnn::UnaryOperation op)
347 {
348  ElementwiseUnaryDescriptor desc = ElementwiseUnaryDescriptor(op);
349  Layer* const layer = graph.AddLayer<armnn::ElementwiseUnaryLayer>(desc, "layer");
350 
351  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
352  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
353 
354  armnn::TensorInfo tensorInfo({ 2, 3 }, DataType);
355  Connect(input, layer, tensorInfo, 0, 0);
356  Connect(layer, output, tensorInfo, 0, 0);
357  CreateTensorHandles(graph, factory);
358 
359  auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);
360  DescriptorType queueDescriptor = workload->GetData();
361 
362  CHECK(queueDescriptor.m_Inputs.size() == 1);
363  CHECK(queueDescriptor.m_Outputs.size() == 1);
364 
365  return workload;
366 }
367 
368 template <typename BatchNormalizationWorkloadType, armnn::DataType DataType>
369 std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWorkloadTest(
370  armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW)
371 {
372  TensorShape tensorShape;
373  switch (dataLayout)
374  {
375  case DataLayout::NHWC:
376  tensorShape = { 2, 4, 4, 3 };
377  break;
378  case DataLayout::NCHW:
379  default:
380  tensorShape = { 2, 3, 4, 4 };
381  }
382 
383  // Creates the layer we're testing.
384  BatchNormalizationDescriptor layerDesc;
385  layerDesc.m_Eps = 0.05f;
386  layerDesc.m_DataLayout = dataLayout;
387 
388  BatchNormalizationLayer* const layer = graph.AddLayer<BatchNormalizationLayer>(layerDesc, "layer");
389 
390  armnn::TensorInfo weightInfo({3}, DataType);
391  layer->m_Mean = std::make_unique<ScopedTensorHandle>(weightInfo);
392  layer->m_Variance = std::make_unique<ScopedTensorHandle>(weightInfo);
393  layer->m_Beta = std::make_unique<ScopedTensorHandle>(weightInfo);
394  layer->m_Gamma = std::make_unique<ScopedTensorHandle>(weightInfo);
395  layer->m_Mean->Allocate();
396  layer->m_Variance->Allocate();
397  layer->m_Beta->Allocate();
398  layer->m_Gamma->Allocate();
399 
400  // Creates extra layers.
401  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
402  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
403 
404  // Connects up.
405  armnn::TensorInfo tensorInfo(tensorShape, DataType);
406  Connect(input, layer, tensorInfo);
407  Connect(layer, output, tensorInfo);
408  CreateTensorHandles(graph, factory);
409 
410  // Makes the workload and checks it.
411  auto workload = MakeAndCheckWorkload<BatchNormalizationWorkloadType>(*layer, factory);
412  BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData();
413  CHECK(queueDescriptor.m_Parameters.m_Eps == 0.05f);
414  CHECK(queueDescriptor.m_Inputs.size() == 1);
415  CHECK(queueDescriptor.m_Outputs.size() == 1);
416  CHECK((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType)));
417  CHECK((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType)));
418  CHECK((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType)));
419  CHECK((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType)));
420  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
421 
422  // Returns so we can do extra, backend-specific tests.
423  return workload;
424 }
425 
426 template <typename BatchNormalizationWorkloadType, armnn::DataType DataType>
427 std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWithBlobWorkloadTest(
428  armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW)
429 {
430  TensorShape tensorShape;
431  switch (dataLayout)
432  {
433  case DataLayout::NHWC:
434  tensorShape = { 2, 4, 4, 3 };
435  break;
436  case DataLayout::NCHW:
437  default:
438  tensorShape = { 2, 3, 4, 4 };
439  }
440 
441  // Creates the layer we're testing.
442  BatchNormalizationDescriptor layerDesc;
443  layerDesc.m_Eps = 0.05f;
444  layerDesc.m_DataLayout = dataLayout;
445 
446  BatchNormalizationLayer* const layer = graph.AddLayer<BatchNormalizationLayer>(layerDesc, "layer");
447 
448  armnn::TensorInfo weightInfo({3}, DataType);
449  layer->m_Mean = std::make_unique<ScopedTensorHandle>(weightInfo);
450  layer->m_Variance = std::make_unique<ScopedTensorHandle>(weightInfo);
451  layer->m_Beta = std::make_unique<ScopedTensorHandle>(weightInfo);
452  layer->m_Gamma = std::make_unique<ScopedTensorHandle>(weightInfo);
453  layer->m_Mean->Allocate();
454  layer->m_Variance->Allocate();
455  layer->m_Beta->Allocate();
456  layer->m_Gamma->Allocate();
457 
458  auto activationDesc = std::make_shared<ActivationDescriptor>();
459  activationDesc->m_A = 10.0f;
460  activationDesc->m_B = 5.0f;
461  activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu;
462 
463  layer->SetAdditionalInfoForObject(activationDesc);
464 
465  // Check that the additional information can be queried from the layer
466  std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>();
467  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f);
468  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f);
469  ARMNN_ASSERT(
470  static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
471  );
472 
473  // Creates extra layers.
474  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
475  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
476 
477  // Connects up.
478  armnn::TensorInfo tensorInfo(tensorShape, DataType);
479  Connect(input, layer, tensorInfo);
480  Connect(layer, output, tensorInfo);
481  CreateTensorHandles(graph, factory);
482 
483  // Makes the workload and checks it.
484  auto workload = MakeAndCheckWorkload<BatchNormalizationWorkloadType>(*layer, factory);
485  BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData();
486  const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>();
487  IgnoreUnused(queueDescBlobPtr);
488  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f);
489  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f);
490  ARMNN_ASSERT(
491  static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
492  );
493 
494  CHECK(queueDescriptor.m_Parameters.m_Eps == 0.05f);
495  CHECK(queueDescriptor.m_Inputs.size() == 1);
496  CHECK(queueDescriptor.m_Outputs.size() == 1);
497  CHECK((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType)));
498  CHECK((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType)));
499  CHECK((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType)));
500  CHECK((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType)));
501  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
502 
503  // Returns so we can do extra, backend-specific tests.
504  return workload;
505 }
506 
507 template <typename Convolution2dWorkload, armnn::DataType DataType>
508 std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadTest(armnn::IWorkloadFactory& factory,
509  armnn::Graph& graph,
510  DataLayout dataLayout = DataLayout::NCHW,
511  const ModelOptions& modelOptions = {})
512 {
513  // Creates the layer we're testing.
514  Convolution2dDescriptor layerDesc;
515  layerDesc.m_PadLeft = 3;
516  layerDesc.m_PadRight = 3;
517  layerDesc.m_PadTop = 1;
518  layerDesc.m_PadBottom = 1;
519  layerDesc.m_StrideX = 2;
520  layerDesc.m_StrideY = 4;
521  layerDesc.m_BiasEnabled = false;
522  layerDesc.m_DataLayout = dataLayout;
523 
524  float inputsQScale = 1.0f;
525  float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0;
526 
527  Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
528 
529  TensorShape weightShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 5, 3} : TensorShape{2, 5, 3, 3};
530  TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3};
531  TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2};
532 
533  armnn::TensorInfo weightsTensorInfo(weightShape, DataType, inputsQScale);
534  weightsTensorInfo.SetConstant();
535 
536  // Creates extra layers.
537  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
538  auto const weights = graph.AddLayer<ConstantLayer>("weights");
539  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
540 
541  weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo);
542  weights->m_LayerOutput->Allocate();
543 
544  // Connects up.
545  Connect(input, layer, TensorInfo(inputShape, DataType, inputsQScale));
546  Connect(weights, layer, weightsTensorInfo, 0, 1);
547  Connect(layer, output, TensorInfo(outputShape, DataType, outputQScale));
548  CreateTensorHandles(graph, factory);
549 
550  // Makes the workload and checks it.
551  auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions);
552 
553  Convolution2dQueueDescriptor queueDescriptor = workload->GetData();
554  CHECK(queueDescriptor.m_Parameters.m_StrideX == 2);
555  CHECK(queueDescriptor.m_Parameters.m_StrideY == 4);
556  CHECK(queueDescriptor.m_Parameters.m_PadLeft == 3);
557  CHECK(queueDescriptor.m_Parameters.m_PadRight == 3);
558  CHECK(queueDescriptor.m_Parameters.m_PadTop == 1);
559  CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1);
560  CHECK(!queueDescriptor.m_Parameters.m_BiasEnabled);
561  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
562 
563  CHECK(queueDescriptor.m_Inputs.size() == 2);
564  CHECK(queueDescriptor.m_Outputs.size() == 1);
565 
566  // Returns so we can do extra, backend-specific tests.
567  return workload;
568 }
569 
570 template<typename Convolution2dWorkload, armnn::DataType DataType>
571 std::unique_ptr<Convolution2dWorkload> CreateConvolution2dFusedActivationWithBlobWorkloadTest(
572  armnn::IWorkloadFactory& factory,
573  armnn::Graph& graph,
574  DataLayout dataLayout = DataLayout::NCHW,
575  const ModelOptions& modelOptions = {})
576 {
577  // Creates the layer we're testing.
578  Convolution2dDescriptor layerDesc;
579  layerDesc.m_PadLeft = 3;
580  layerDesc.m_PadRight = 3;
581  layerDesc.m_PadTop = 1;
582  layerDesc.m_PadBottom = 1;
583  layerDesc.m_StrideX = 2;
584  layerDesc.m_StrideY = 4;
585  layerDesc.m_BiasEnabled = true;
586  layerDesc.m_DataLayout = dataLayout;
587 
588  float inputsQScale = 1.0f;
589  float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0;
590 
591  Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
592 
593  TensorShape weightShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 5, 3} : TensorShape{2, 5, 3, 3};
594  TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3};
595  TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2};
596 
597  armnn::TensorInfo weightsTensorInfo(weightShape, DataType, inputsQScale);
598  weightsTensorInfo.SetConstant();
599  armnn::TensorInfo biasTensorInfo({2}, DataType, inputsQScale);
600  biasTensorInfo.SetConstant();
601 
602  auto activationDesc = std::make_shared<ActivationDescriptor>();
603  activationDesc->m_A = 10.0f;
604  activationDesc->m_B = 5.0f;
605  activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu;
606 
607  layer->SetAdditionalInfoForObject(activationDesc);
608 
609  // Check that the additional information can be queried from the layer
610  std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>();
611 
612  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f);
613  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f);
614  ARMNN_ASSERT(
615  static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
616  );
617 
618  // Creates extra layers.
619  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
620  auto const weights = graph.AddLayer<ConstantLayer>("weights");
621  auto const bias = graph.AddLayer<ConstantLayer>("bias");
622  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
623 
624  weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo);
625  weights->m_LayerOutput->Allocate();
626  bias->m_LayerOutput = std::make_unique<ScopedTensorHandle>(biasTensorInfo);
627  bias->m_LayerOutput->Allocate();
628 
629  // Connects up.
630  Connect(input, layer, TensorInfo(inputShape, DataType, inputsQScale));
631  Connect(weights, layer, weightsTensorInfo, 0, 1);
632  Connect(bias, layer, biasTensorInfo, 0, 2);
633  Connect(layer, output, TensorInfo(outputShape, DataType, outputQScale));
634  CreateTensorHandles(graph, factory);
635 
636  // Makes the workload and checks it.
637  auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions);
638 
639  Convolution2dQueueDescriptor queueDescriptor = workload->GetData();
640  const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>();
641  IgnoreUnused(queueDescBlobPtr);
642  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f);
643  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f);
644  ARMNN_ASSERT(
645  static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
646  );
647 
648  CHECK(queueDescriptor.m_Parameters.m_StrideX == 2);
649  CHECK(queueDescriptor.m_Parameters.m_StrideY == 4);
650  CHECK(queueDescriptor.m_Parameters.m_PadLeft == 3);
651  CHECK(queueDescriptor.m_Parameters.m_PadRight == 3);
652  CHECK(queueDescriptor.m_Parameters.m_PadTop == 1);
653  CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1);
654  CHECK(queueDescriptor.m_Parameters.m_BiasEnabled);
655  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
656 
657  CHECK(queueDescriptor.m_Outputs.size() == 1);
658  CHECK(queueDescriptor.m_Inputs.size() == 3);
659 
660  // Returns so we can do extra, backend-specific tests.
661  return workload;
662 }
663 
664 template <typename Convolution2dWorkload, armnn::DataType DataType>
665 std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadFastMathTest(armnn::IWorkloadFactory& factory,
666  armnn::Graph& graph,
667  DataLayout dataLayout = DataLayout::NCHW,
668  const ModelOptions& modelOptions = {})
669 {
670  // Creates the layer we're testing.
671  Convolution2dDescriptor layerDesc;
672  layerDesc.m_PadLeft = 0;
673  layerDesc.m_PadRight = 0;
674  layerDesc.m_PadTop = 0;
675  layerDesc.m_PadBottom = 0;
676  layerDesc.m_StrideX = 1;
677  layerDesc.m_StrideY = 1;
678  layerDesc.m_BiasEnabled = true;
679  layerDesc.m_DataLayout = dataLayout;
680 
681  float inputsQScale = 1.0f;
682  float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0;
683 
684  Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
685 
686  TensorShape weightShape = TensorShape{ 32, 32, 3, 3 };
687  TensorShape biasShape = TensorShape{ 32 };
688  TensorShape inputShape = TensorShape{ 1, 32, 149, 149 };
689  TensorShape outputShape = TensorShape{ 1, 32, 147, 147 };
690 
691  armnn::TensorInfo weightsTensorInfo(weightShape, DataType, inputsQScale);
692  weightsTensorInfo.SetConstant();
693  armnn::TensorInfo biasTensorInfo(biasShape, DataType, inputsQScale);
694  biasTensorInfo.SetConstant();
695 
696  // Creates extra layers.
697  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
698  auto const weights = graph.AddLayer<ConstantLayer>("weights");
699  auto const bias = graph.AddLayer<ConstantLayer>("bias");
700  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
701 
702  // Connects up.
703  Connect(input, layer, TensorInfo(inputShape, DataType));
704  Connect(weights, layer, weightsTensorInfo, 0, 1);
705  Connect(bias, layer, biasTensorInfo, 0, 2);
706  Connect(layer, output, TensorInfo(outputShape, DataType, outputQScale));
707  CreateTensorHandles(graph, factory);
708 
709  // Makes the workload and checks it.
710  auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions);
711 
712  Convolution2dQueueDescriptor queueDescriptor = workload->GetData();
713  CHECK(queueDescriptor.m_Parameters.m_StrideX == 1);
714  CHECK(queueDescriptor.m_Parameters.m_StrideY == 1);
715  CHECK(queueDescriptor.m_Parameters.m_PadLeft == 0);
716  CHECK(queueDescriptor.m_Parameters.m_PadRight == 0);
717  CHECK(queueDescriptor.m_Parameters.m_PadTop == 0);
718  CHECK(queueDescriptor.m_Parameters.m_PadBottom == 0);
719  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
720 
721  CHECK(queueDescriptor.m_Inputs.size() == 3);
722  CHECK(queueDescriptor.m_Outputs.size() == 1);
723 
724  // Returns so we can do extra, backend-specific tests.
725  return workload;
726 }
727 
728 template <typename LstmWorkload>
729 std::unique_ptr<LstmWorkload> CreateLstmWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph)
730 {
731  // This parameter setting is for withCifgWithPeepholeNoProjection
732  LstmDescriptor layerDesc;
733  layerDesc.m_ActivationFunc = 4;
734  layerDesc.m_ClippingThresCell = 0.0f;
735  layerDesc.m_ClippingThresProj = 0.0f;
736  layerDesc.m_CifgEnabled = true;
737  layerDesc.m_PeepholeEnabled = true;
738  layerDesc.m_ProjectionEnabled = false;
739 
740  LstmLayer* const layer = graph.AddLayer<LstmLayer>(layerDesc, "layer");
741  unsigned int batchSize = 2;
742  unsigned int inputSize = 2;
743  unsigned int numUnits = 4;
744  unsigned int outputSize = 4;
745 
746  layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedTensorHandle>
747  (TensorInfo({ numUnits, inputSize }, DataType::Float32));
748  layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedTensorHandle>
749  (TensorInfo({ numUnits, inputSize }, DataType::Float32));
750  layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedTensorHandle>
751  (TensorInfo({ numUnits, inputSize }, DataType::Float32));
752  layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<ScopedTensorHandle>
753  (TensorInfo({ numUnits, outputSize }, DataType::Float32));
754  layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<ScopedTensorHandle>
755  (TensorInfo({ numUnits, outputSize }, DataType::Float32));
756  layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<ScopedTensorHandle>
757  (TensorInfo({ numUnits, outputSize }, DataType::Float32));
758  layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle>
759  (TensorInfo({ numUnits }, DataType::Float32));
760  layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedTensorHandle>
761  (TensorInfo({ numUnits }, DataType::Float32));
762  layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle>
763  (TensorInfo({ numUnits }, DataType::Float32));
764 
765  layer->m_BasicParameters.m_InputToForgetWeights->Allocate();
766  layer->m_BasicParameters.m_InputToCellWeights->Allocate();
767  layer->m_BasicParameters.m_InputToOutputWeights->Allocate();
768  layer->m_BasicParameters.m_RecurrentToForgetWeights->Allocate();
769  layer->m_BasicParameters.m_RecurrentToCellWeights->Allocate();
770  layer->m_BasicParameters.m_RecurrentToOutputWeights->Allocate();
771  layer->m_BasicParameters.m_ForgetGateBias->Allocate();
772  layer->m_BasicParameters.m_CellBias->Allocate();
773  layer->m_BasicParameters.m_OutputGateBias->Allocate();
774 
775 
776  if (layerDesc.m_PeepholeEnabled)
777  {
778  layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<ScopedTensorHandle>
779  (TensorInfo({ numUnits }, DataType::Float32));
780  layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<ScopedTensorHandle>
781  (TensorInfo({ numUnits }, DataType::Float32));
782  layer->m_PeepholeParameters.m_CellToForgetWeights->Allocate();
783  layer->m_PeepholeParameters.m_CellToOutputWeights->Allocate();
784  }
785 
786  // create input and output layers
787  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
788  Layer* const outputStateIn = graph.AddLayer<InputLayer>(1, "outputStateIn");
789  Layer* const cellStateIn = graph.AddLayer<InputLayer>(2, "cellStateIn");
790  Layer* const scratchBuffer = graph.AddLayer<OutputLayer>(0, "scratchBuffer");
791  Layer* const outputStateOut = graph.AddLayer<OutputLayer>(1, "outputStateOut");
792  Layer* const cellStateOut = graph.AddLayer<OutputLayer>(2, "cellStateOut");
793  Layer* const output = graph.AddLayer<OutputLayer>(3, "output");
794 
795  // connect up
796  armnn::TensorInfo lstmTensorInfo1({ batchSize, inputSize }, DataType::Float32);
797  armnn::TensorInfo lstmTensorInfo2({ batchSize, numUnits}, DataType::Float32);
798  armnn::TensorInfo lstmTensorInfo3({ batchSize, outputSize }, DataType::Float32);
799  armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * (layerDesc.m_CifgEnabled ? 3 : 4) },
800  DataType::Float32);
801  Connect(input, layer, lstmTensorInfo1, 0, 0);
802  Connect(cellStateIn, layer, lstmTensorInfo2, 0, 1);
803  Connect(outputStateIn, layer, lstmTensorInfo3, 0, 2);
804  Connect(layer, scratchBuffer, lstmTensorInfoScratchBuff, 0, 0);
805  Connect(layer, outputStateOut, lstmTensorInfo3, 1, 0);
806  Connect(layer, cellStateOut, lstmTensorInfo2, 2, 0);
807  Connect(layer, output, lstmTensorInfo3, 3, 0);
808 
809  CreateTensorHandles(graph, factory);
810 
811  // make the workload and check it
812  auto workload = MakeAndCheckWorkload<LstmWorkload>(*layer, factory);
813  LstmQueueDescriptor queueDescriptor = workload->GetData();
814  CHECK(queueDescriptor.m_Parameters.m_ActivationFunc == 4);
815  CHECK(queueDescriptor.m_Parameters.m_ClippingThresCell == 0.0f);
816  CHECK(queueDescriptor.m_Parameters.m_ClippingThresProj == 0.0f);
817  CHECK(queueDescriptor.m_Inputs.size() == 3);
818  CHECK(queueDescriptor.m_Outputs.size() == 4);
819 
820  CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == TensorInfo({ numUnits, inputSize },
821  DataType::Float32)));
822  CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == TensorInfo({ numUnits },
823  DataType::Float32)));
824  CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == TensorInfo({ numUnits }, DataType::Float32)));
825  return workload;
826 }
827 
828 template <typename QuantizedLstmWorkload>
829 std::unique_ptr<QuantizedLstmWorkload> CreateQuantizedLstmWorkloadTest(armnn::IWorkloadFactory& factory,
830  armnn::Graph& graph)
831 {
832  auto layer = graph.AddLayer<QuantizedLstmLayer>("quantizedLstmlayer");
833  unsigned int numBatches = 2;
834  unsigned int inputSize = 2;
835  unsigned int outputSize = 4;
836 
837  // Scale/Offset for input/output, cellState In/Out, weights, bias
838  float inputOutputScale = 0.0078125f;
839  int32_t inputOutputOffset = 128;
840 
841  float cellStateScale = 0.00048828125f;
842  int32_t cellStateOffset = 0;
843 
844  float weightsScale = 0.00408021f;
845  int32_t weightsOffset = 100;
846 
847  float biasScale = 3.1876640625e-05f;
848  int32_t biasOffset = 0;
849 
850  // Weights and bias tensor and quantization info
851  armnn::TensorInfo inputWeightsInfo({outputSize, inputSize},
852  armnn::DataType::QAsymmU8,
853  weightsScale,
854  weightsOffset);
855 
856  armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize},
857  armnn::DataType::QAsymmU8,
858  weightsScale,
859  weightsOffset);
860 
861  armnn::TensorInfo biasInfo({outputSize},
862  armnn::DataType::Signed32,
863  biasScale,
864  biasOffset);
865 
866  // Weights and bias
867  layer->m_QuantizedLstmParameters.m_InputToInputWeights =
868  std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
869  layer->m_QuantizedLstmParameters.m_InputToForgetWeights =
870  std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
871  layer->m_QuantizedLstmParameters.m_InputToCellWeights =
872  std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
873  layer->m_QuantizedLstmParameters.m_InputToOutputWeights =
874  std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
875 
876  layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights =
877  std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
878  layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights =
879  std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
880  layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights =
881  std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
882  layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights =
883  std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
884 
885  layer->m_QuantizedLstmParameters.m_InputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
886  layer->m_QuantizedLstmParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
887  layer->m_QuantizedLstmParameters.m_CellBias = std::make_unique<ScopedTensorHandle>(biasInfo);
888  layer->m_QuantizedLstmParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
889 
890  // Allocate weights and bias
891  layer->m_QuantizedLstmParameters.m_InputToInputWeights->Allocate();
892  layer->m_QuantizedLstmParameters.m_InputToForgetWeights->Allocate();
893  layer->m_QuantizedLstmParameters.m_InputToCellWeights->Allocate();
894  layer->m_QuantizedLstmParameters.m_InputToOutputWeights->Allocate();
895 
896  layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights->Allocate();
897  layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights->Allocate();
898  layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights->Allocate();
899  layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights->Allocate();
900 
901  layer->m_QuantizedLstmParameters.m_InputGateBias->Allocate();
902  layer->m_QuantizedLstmParameters.m_ForgetGateBias->Allocate();
903  layer->m_QuantizedLstmParameters.m_CellBias->Allocate();
904  layer->m_QuantizedLstmParameters.m_OutputGateBias->Allocate();
905 
906  // Create input and output layers
907  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
908  Layer* const cellStateIn = graph.AddLayer<InputLayer>(1, "cellStateIn");
909  Layer* const outputStateIn = graph.AddLayer<InputLayer>(2, "outputStateIn");
910 
911  Layer* const cellStateOut = graph.AddLayer<OutputLayer>(0, "cellStateOut");
912  Layer* const outputStateOut = graph.AddLayer<OutputLayer>(1, "outputStateOut");
913 
914  // Input/output tensor info and quantization info
915  armnn::TensorInfo inputInfo({numBatches , inputSize},
916  armnn::DataType::QAsymmU8,
917  inputOutputScale,
918  inputOutputOffset);
919 
920  armnn::TensorInfo cellStateInfo({numBatches , outputSize},
921  armnn::DataType::QSymmS16,
922  cellStateScale,
923  cellStateOffset);
924 
925  armnn::TensorInfo outputStateInfo({numBatches , outputSize},
926  armnn::DataType::QAsymmU8,
927  inputOutputScale,
928  inputOutputOffset);
929 
930  // Connect input/output slots
931  Connect(input, layer, inputInfo, 0, 0);
932  Connect(cellStateIn, layer, cellStateInfo, 0, 1);
933  Connect(outputStateIn, layer, outputStateInfo, 0, 2);
934 
935  Connect(layer, cellStateOut, cellStateInfo, 0, 0);
936  Connect(layer, outputStateOut, outputStateInfo, 1, 0);
937 
938  CreateTensorHandles(graph, factory);
939 
940  // Create workload and check layer support
941  auto workload = MakeAndCheckWorkload<QuantizedLstmWorkload>(*layer, factory);
942  QuantizedLstmQueueDescriptor queueDescriptor = workload->GetData();
943 
944  // Validate input/output sizes
945  CHECK(queueDescriptor.m_Inputs.size() == 3);
946  CHECK(queueDescriptor.m_Outputs.size() == 2);
947 
948  // Validate weight tensor info
949  CHECK((queueDescriptor.m_InputToInputWeights->GetTensorInfo() == inputWeightsInfo));
950  CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo));
951  CHECK((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo));
952  CHECK((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo));
953 
954  CHECK((queueDescriptor.m_RecurrentToInputWeights->GetTensorInfo() == recurrentWeightsInfo));
955  CHECK((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo));
956  CHECK((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo));
957  CHECK((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo));
958 
959  CHECK((queueDescriptor.m_InputGateBias->GetTensorInfo() == biasInfo));
960  CHECK((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo));
961  CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo));
962  CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo));
963 
964  return workload;
965 }
966 
967 template <typename QLstmWorkload>
968 std::unique_ptr<QLstmWorkload> CreateQLstmWorkloadTest(armnn::IWorkloadFactory& factory,
969  armnn::Graph& graph)
970 {
971  QLstmDescriptor layerDesc;
972  layerDesc.m_CifgEnabled = true;
973  layerDesc.m_PeepholeEnabled = false;
974  layerDesc.m_ProjectionEnabled = false;
975  layerDesc.m_LayerNormEnabled = true;
976 
977  layerDesc.m_CellClip = 0.0f;
978  layerDesc.m_ProjectionClip = 0.0f;
979 
980  layerDesc.m_HiddenStateZeroPoint = 0;
981  layerDesc.m_HiddenStateScale = 0.007f;
982 
983  layerDesc.m_InputIntermediateScale = 0.007059f;
984  layerDesc.m_ForgetIntermediateScale = 0.007812f;
985  layerDesc.m_CellIntermediateScale = 0.007059f;
986  layerDesc.m_OutputIntermediateScale = 0.007812f;
987 
988  QLstmLayer* const layer = graph.AddLayer<QLstmLayer>(layerDesc, "qLstm");
989 
990  unsigned int numBatches = 2;
991  unsigned int inputSize = 4;
992  unsigned int numUnits = 4;
993  unsigned int outputSize = 4;
994 
995  // Scale/Offset quantization info
996  float inputScale = 0.0078125f;
997  int32_t inputOffset = 0;
998 
999  // if (!projectionEnabled) outputScale == hiddenStateScale
1000  float outputScale = layerDesc.m_HiddenStateScale;
1001  int32_t outputOffset = layerDesc.m_HiddenStateZeroPoint;
1002 
1003  float cellStateScale = 3.05176e-05f;
1004  int32_t cellStateOffset = 0;
1005 
1006  float weightsScale = 0.00784314f;
1007  int32_t weightsOffset = 0;
1008 
1009  float layerNormScale = 3.05182e-05f;
1010  int32_t layerNormOffset = 0;
1011 
1012  float biasScale = layerNormScale / 1024;
1013  int32_t biasOffset = 0;
1014 
1015  // Weights and bias tensor and quantization info
1016  armnn::TensorInfo inputWeightsInfo({outputSize, inputSize},
1017  armnn::DataType::QSymmS8,
1018  weightsScale,
1019  weightsOffset);
1020 
1021  armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize},
1022  armnn::DataType::QSymmS8,
1023  weightsScale,
1024  weightsOffset);
1025 
1026  armnn::TensorInfo biasInfo({outputSize}, armnn::DataType::Signed32, biasScale, biasOffset);
1027 
1028  armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, layerNormOffset);
1029 
1030  // Create and allocate tensors
1031  layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
1032  layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
1033  layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
1034 
1035  layer->m_BasicParameters.m_RecurrentToForgetWeights =
1036  std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
1037  layer->m_BasicParameters.m_RecurrentToCellWeights =
1038  std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
1039  layer->m_BasicParameters.m_RecurrentToOutputWeights =
1040  std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
1041 
1042  layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
1043  layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedTensorHandle>(biasInfo);
1044  layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
1045 
1046  layer->m_LayerNormParameters.m_ForgetLayerNormWeights =
1047  std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo);
1048  layer->m_LayerNormParameters.m_CellLayerNormWeights =
1049  std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo);
1050  layer->m_LayerNormParameters.m_OutputLayerNormWeights =
1051  std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo);
1052 
1053  layer->m_BasicParameters.m_InputToForgetWeights->Allocate();
1054  layer->m_BasicParameters.m_InputToCellWeights->Allocate();
1055  layer->m_BasicParameters.m_InputToOutputWeights->Allocate();
1056 
1057  layer->m_BasicParameters.m_RecurrentToForgetWeights->Allocate();
1058  layer->m_BasicParameters.m_RecurrentToCellWeights->Allocate();
1059  layer->m_BasicParameters.m_RecurrentToOutputWeights->Allocate();
1060 
1061  layer->m_BasicParameters.m_ForgetGateBias->Allocate();
1062  layer->m_BasicParameters.m_CellBias->Allocate();
1063  layer->m_BasicParameters.m_OutputGateBias->Allocate();
1064 
1065  layer->m_LayerNormParameters.m_ForgetLayerNormWeights->Allocate();
1066  layer->m_LayerNormParameters.m_CellLayerNormWeights->Allocate();
1067  layer->m_LayerNormParameters.m_OutputLayerNormWeights->Allocate();
1068 
1069  // Input and output layers
1070  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1071  Layer* const outputStateIn = graph.AddLayer<InputLayer>(1, "outputStateIn");
1072  Layer* const cellStateIn = graph.AddLayer<InputLayer>(2, "cellStateIn");
1073 
1074  Layer* const outputStateOut = graph.AddLayer<OutputLayer>(0, "outputStateOut");
1075  Layer* const cellStateOut = graph.AddLayer<OutputLayer>(1, "cellStateOut");
1076  Layer* const output = graph.AddLayer<OutputLayer>(2, "output");
1077 
1078  // Input/Output tensor info
1079  armnn::TensorInfo inputInfo({numBatches , inputSize},
1080  armnn::DataType::QAsymmS8,
1081  inputScale,
1082  inputOffset);
1083 
1084  armnn::TensorInfo cellStateInfo({numBatches , numUnits},
1085  armnn::DataType::QSymmS16,
1086  cellStateScale,
1087  cellStateOffset);
1088 
1089  armnn::TensorInfo outputStateInfo({numBatches , outputSize},
1090  armnn::DataType::QAsymmS8,
1091  outputScale,
1092  outputOffset);
1093 
1094  // Connect layers to slots
1095  Connect(input, layer, inputInfo, 0, 0);
1096  Connect(outputStateIn, layer, outputStateInfo, 0, 1);
1097  Connect(cellStateIn, layer, cellStateInfo, 0, 2);
1098 
1099  Connect(layer, outputStateOut, outputStateInfo, 0, 0);
1100  Connect(layer, cellStateOut, cellStateInfo, 1, 0);
1101  Connect(layer, output, outputStateInfo, 2, 0);
1102 
1103  CreateTensorHandles(graph, factory);
1104 
1105  // Create and check workload
1106  auto workload = MakeAndCheckWorkload<QLstmWorkload>(*layer, factory);
1107  QLstmQueueDescriptor queueDescriptor = workload->GetData();
1108  CHECK(queueDescriptor.m_Parameters.m_CellClip == 0.0f);
1109  CHECK(queueDescriptor.m_Parameters.m_ProjectionClip == 0.0f);
1110  CHECK(queueDescriptor.m_Inputs.size() == 3);
1111  CHECK(queueDescriptor.m_Outputs.size() == 3);
1112 
1113  CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo));
1114  CHECK((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo));
1115  CHECK((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo));
1116 
1117  CHECK((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo));
1118  CHECK((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo));
1119  CHECK((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo));
1120 
1121  CHECK((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo));
1122  CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo));
1123  CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo));
1124 
1125  return workload;
1126 }
1127 
1128 template<typename Convolution2dWorkload, armnn::DataType DataType>
1129 std::unique_ptr<Convolution2dWorkload> CreateDirectConvolution2dWorkloadTest(armnn::IWorkloadFactory& factory,
1130  armnn::Graph& graph)
1131 {
1132  // Creates the layer we're testing.
1133  Convolution2dDescriptor layerDesc;
1134  layerDesc.m_PadLeft = 1;
1135  layerDesc.m_PadRight = 1;
1136  layerDesc.m_PadTop = 1;
1137  layerDesc.m_PadBottom = 1;
1138  layerDesc.m_StrideX = 1;
1139  layerDesc.m_StrideY = 1;
1140  layerDesc.m_BiasEnabled = true;
1141 
1142  Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
1143 
1144  float inputsQScale = 1.0f;
1145  float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0;
1146 
1147  TensorShape biasShape = TensorShape{ 2 };
1148  TensorShape weightShape = TensorShape{ 2, 3, 3, 3 };
1149  armnn::TensorInfo weightsTensorInfo(weightShape, DataType, inputsQScale);
1150  weightsTensorInfo.SetConstant();
1151  armnn::TensorInfo biasTensorInfo(biasShape, GetBiasDataType(DataType), inputsQScale);
1152  biasTensorInfo.SetConstant();
1153 
1154  // Creates extra layers.
1155  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1156  auto const weights = graph.AddLayer<ConstantLayer>("weights");
1157  auto const bias = graph.AddLayer<ConstantLayer>("bias");
1158  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1159 
1160  weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo);
1161  weights->m_LayerOutput->Allocate();
1162  bias->m_LayerOutput = std::make_unique<ScopedTensorHandle>(biasTensorInfo);
1163  bias->m_LayerOutput->Allocate();
1164 
1165  // Connects up.
1166  Connect(input, layer, TensorInfo({2, 3, 6, 6}, DataType, inputsQScale));
1167  Connect(weights, layer, weightsTensorInfo, 0, 1);
1168  Connect(bias, layer, biasTensorInfo, 0, 2);
1169  Connect(layer, output, TensorInfo({2, 2, 6, 6}, DataType, outputQScale));
1170  CreateTensorHandles(graph, factory);
1171 
1172  // Makes the workload and checks it.
1173  auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory);
1174 
1175  Convolution2dQueueDescriptor queueDescriptor = workload->GetData();
1176  CHECK(queueDescriptor.m_Parameters.m_StrideX == 1);
1177  CHECK(queueDescriptor.m_Parameters.m_StrideY == 1);
1178  CHECK(queueDescriptor.m_Parameters.m_PadLeft == 1);
1179  CHECK(queueDescriptor.m_Parameters.m_PadRight == 1);
1180  CHECK(queueDescriptor.m_Parameters.m_PadTop == 1);
1181  CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1);
1182  CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true);
1183 
1184  CHECK(queueDescriptor.m_Inputs.size() == 3);
1185  CHECK(queueDescriptor.m_Outputs.size() == 1);
1186 
1187  // Returns so we can do extra, backend-specific tests.
1188  return workload;
1189 }
1190 
1191 template <typename DepthwiseConvolution2dFloat32Workload, armnn::DataType DataType>
1192 std::unique_ptr<DepthwiseConvolution2dFloat32Workload> CreateDepthwiseConvolution2dWorkloadTest(
1193  armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW)
1194 {
1195  // Creates the layer we're testing.
1196  DepthwiseConvolution2dDescriptor layerDesc;
1197  layerDesc.m_PadLeft = 1;
1198  layerDesc.m_PadRight = 2;
1199  layerDesc.m_PadTop = 1;
1200  layerDesc.m_PadBottom = 2;
1201  layerDesc.m_StrideX = 1;
1202  layerDesc.m_StrideY = 1;
1203  layerDesc.m_BiasEnabled = false;
1204  layerDesc.m_DataLayout = dataLayout;
1205 
1206  float inputsQScale = 1.0f;
1207  float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0;
1208 
1209  TensorShape weightShape({1, 4, 4, 2});
1210  TensorShape inputShape = (dataLayout == DataLayout::NCHW) ?
1211  TensorShape{ 2, 2, 5, 5 } : TensorShape{ 2, 5, 5, 2 };
1212  TensorShape outputShape = (dataLayout == DataLayout::NCHW) ?
1213  TensorShape{ 2, 2, 5, 5 } : TensorShape{ 2, 5, 5, 2 };
1214 
1215  DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer");
1216 
1217 
1218  // Creates extra layers.
1219  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1220  Layer* const weights = graph.AddLayer<ConstantLayer>("weights");
1221  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1222 
1223  // Connects up.
1224  Connect(input, layer, TensorInfo(inputShape, DataType, inputsQScale));
1225  Connect(weights, layer, TensorInfo(weightShape, DataType, inputsQScale, 0.0f, true), 0, 1);
1226  Connect(layer, output, TensorInfo(outputShape, DataType, outputQScale));
1227  CreateTensorHandles(graph, factory);
1228 
1229  // Makes the workload and checks it.
1230  auto workload = MakeAndCheckWorkload<DepthwiseConvolution2dFloat32Workload>(*layer, factory);
1231 
1232  DepthwiseConvolution2dQueueDescriptor queueDescriptor = workload->GetData();
1233  CHECK(queueDescriptor.m_Parameters.m_StrideX == 1);
1234  CHECK(queueDescriptor.m_Parameters.m_StrideY == 1);
1235  CHECK(queueDescriptor.m_Parameters.m_PadLeft == 1);
1236  CHECK(queueDescriptor.m_Parameters.m_PadRight == 2);
1237  CHECK(queueDescriptor.m_Parameters.m_PadTop == 1);
1238  CHECK(queueDescriptor.m_Parameters.m_PadBottom == 2);
1239  CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == false);
1240  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
1241 
1242  CHECK(queueDescriptor.m_Inputs.size() == 2);
1243  CHECK(queueDescriptor.m_Outputs.size() == 1);
1244 
1245  // Returns so we can do extra, backend-specific tests.
1246  return workload;
1247 }
1248 
1249 template <typename FullyConnectedWorkload, armnn::DataType DataType>
1250 std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadTest(armnn::IWorkloadFactory& factory,
1251  armnn::Graph& graph)
1252 {
1253  // Creates the layer we're testing.
1254  FullyConnectedDescriptor layerDesc;
1255  layerDesc.m_BiasEnabled = false;
1256  layerDesc.m_TransposeWeightMatrix = true;
1257 
1258  FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
1259 
1260  float inputsQScale = 1.0f;
1261  float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0;
1262 
1263  armnn::TensorInfo weightsTensorInfo({7, 20}, DataType, inputsQScale);
1264  weightsTensorInfo.SetConstant();
1265 
1266  // Creates extra layers.
1267  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1268  auto const weights = graph.AddLayer<ConstantLayer>("weights");
1269  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1270 
1271  weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo);
1272  weights->m_LayerOutput->Allocate();
1273 
1274  // Connects up.
1275  Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0);
1276  Connect(weights, layer, weightsTensorInfo, 0, 1);
1277  Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale));
1278  CreateTensorHandles(graph, factory);
1279 
1280  // Makes the workload and checks it.
1281  auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory);
1282 
1283  FullyConnectedQueueDescriptor queueDescriptor = workload->GetData();
1284  CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true);
1285 
1286  CHECK(queueDescriptor.m_Inputs.size() == 2);
1287  CHECK(queueDescriptor.m_Outputs.size() == 1);
1288 
1289  // Returns so we can do extra, backend-specific tests.
1290  return workload;
1291 }
1292 
1293 template <typename FullyConnectedWorkload, armnn::DataType DataType>
1294 std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWithBlobWorkloadTest
1295  (armnn::IWorkloadFactory& factory,
1296  armnn::Graph& graph)
1297 {
1298  // Creates the layer we're testing.
1299  FullyConnectedDescriptor layerDesc;
1300  layerDesc.m_BiasEnabled = true;
1301  layerDesc.m_TransposeWeightMatrix = true;
1302 
1303  FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
1304 
1305  float inputsQScale = 1.0f;
1306  float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0;
1307 
1308  armnn::TensorInfo weightsTensorInfo({7, 20}, DataType, inputsQScale);
1309  armnn::TensorInfo biasesTensorInfo({7}, GetBiasDataType(DataType), inputsQScale);
1310  weightsTensorInfo.SetConstant();
1311  biasesTensorInfo.SetConstant();
1312 
1313  auto activationDesc = std::make_shared<ActivationDescriptor>();
1314  activationDesc->m_A = 10.0f;
1315  activationDesc->m_B = 5.0f;
1316  activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu;
1317 
1318  layer->SetAdditionalInfoForObject(activationDesc);
1319 
1320  // Check that the additional information can be queried from the layer
1321  std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>();
1322  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f);
1323  ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f);
1324  ARMNN_ASSERT(static_cast<ActivationFunction>(activationDescPtr->m_Function) ==
1325  armnn::ActivationFunction::BoundedReLu);
1326 
1327  // Creates extra layers.
1328  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1329  auto const weights = graph.AddLayer<ConstantLayer>("weights");
1330  auto const biases = graph.AddLayer<ConstantLayer>("biases");
1331  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1332 
1333  weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo);
1334  weights->m_LayerOutput->Allocate();
1335  biases->m_LayerOutput = std::make_unique<ScopedTensorHandle>(biasesTensorInfo);
1336  biases->m_LayerOutput->Allocate();
1337 
1338  // Connects up.
1339  Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0);
1340  Connect(weights, layer, weightsTensorInfo, 0, 1);
1341  Connect(biases, layer, biasesTensorInfo, 0, 2);
1342  Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale));
1343  CreateTensorHandles(graph, factory);
1344 
1345  // Makes the workload and checks it.
1346  auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory);
1347 
1348  FullyConnectedQueueDescriptor queueDescriptor = workload->GetData();
1349 
1350  const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>();
1351  IgnoreUnused(queueDescBlobPtr);
1352 
1353  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f);
1354  ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f);
1355  ARMNN_ASSERT(
1356  static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu
1357  );
1358 
1359  CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true);
1360  CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true);
1361  CHECK(queueDescriptor.m_Inputs.size() == 3);
1362  CHECK(queueDescriptor.m_Outputs.size() == 1);
1363 
1364  // Returns so we can do extra, backend-specific tests.
1365  return workload;
1366 }
1367 
1368 template <typename FullyConnectedWorkload, armnn::DataType DataType>
1369 std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadWeightsBiasesAsInputsTest
1370  (armnn::IWorkloadFactory& factory,
1371  armnn::Graph& graph)
1372 {
1373  // Creates the layer we're testing.
1374  FullyConnectedDescriptor layerDesc;
1375  layerDesc.m_BiasEnabled = true;
1376  layerDesc.m_TransposeWeightMatrix = true;
1377  layerDesc.m_ConstantWeights = false;
1378 
1379  FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
1380 
1381  float inputsQScale = 1.0f;
1382  float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 1.0;
1383 
1384  // Creates extra layers with weights and biases as input layers.
1385  Layer* const input = graph.AddLayer<InputLayer>(1, "input");
1386  Layer* const weights = graph.AddLayer<InputLayer>(2, "weights");
1387  Layer* const biases = graph.AddLayer<InputLayer>(3, "biases");
1388  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1389 
1390  // Connects up.
1391  Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0);
1392  Connect(weights, layer, TensorInfo({7, 20}, DataType, inputsQScale), 0, 1);
1393  Connect(biases, layer, TensorInfo({7}, GetBiasDataType(DataType), inputsQScale), 0, 2);
1394  Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale));
1395  CreateTensorHandles(graph, factory);
1396 
1397  // Makes the workload and checks it.
1398  auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory);
1399 
1400  FullyConnectedQueueDescriptor queueDescriptor = workload->GetData();
1401 
1402  CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true);
1403  CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true);
1404  CHECK(queueDescriptor.m_Parameters.m_ConstantWeights == false);
1405  CHECK(queueDescriptor.m_Inputs.size() == 3);
1406  CHECK(queueDescriptor.m_Outputs.size() == 1);
1407 
1408  // Returns so we can do extra, backend-specific tests.
1409  return workload;
1410 }
1411 
1412 
1413 template <typename NormalizationWorkload, armnn::DataType DataType>
1414 std::unique_ptr<NormalizationWorkload> CreateNormalizationWorkloadTest(armnn::IWorkloadFactory& factory,
1415  armnn::Graph& graph,
1416  DataLayout dataLayout = DataLayout::NCHW)
1417 {
1418  // Creates the layer we're testing.
1419  NormalizationDescriptor layerDesc;
1420  layerDesc.m_NormChannelType = NormalizationAlgorithmChannel::Across;
1421  layerDesc.m_NormMethodType = NormalizationAlgorithmMethod::LocalBrightness;
1422  layerDesc.m_NormSize = 3;
1423  layerDesc.m_Alpha = 0.5f;
1424  layerDesc.m_Beta = -1.0f;
1425  layerDesc.m_K = 0.2f;
1426  layerDesc.m_DataLayout = dataLayout;
1427 
1428  NormalizationLayer* layer = graph.AddLayer<NormalizationLayer>(layerDesc, "layer");
1429 
1430  // Creates extra layers.
1431  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1432  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1433 
1434  TensorShape inputShape = (dataLayout == DataLayout::NCHW) ?
1435  TensorShape{ 3, 5, 5, 1 } : TensorShape{ 3, 1, 5, 5 };
1436  TensorShape outputShape = (dataLayout == DataLayout::NCHW) ?
1437  TensorShape{ 3, 5, 5, 1 } : TensorShape{ 3, 1, 5, 5 };
1438 
1439  // Connects up.
1440  armnn::TensorInfo inputTensorInfo(inputShape, DataType);
1441  armnn::TensorInfo outputTensorInfo(outputShape, DataType);
1442  Connect(input, layer, inputTensorInfo);
1443  Connect(layer, output, outputTensorInfo);
1444  CreateTensorHandles(graph, factory);
1445 
1446  // Makes the workload and checks it.
1447  auto workload = MakeAndCheckWorkload<NormalizationWorkload>(*layer, factory);
1448 
1449  NormalizationQueueDescriptor queueDescriptor = workload->GetData();
1450  CHECK((queueDescriptor.m_Parameters.m_NormChannelType == NormalizationAlgorithmChannel::Across));
1451  CHECK((queueDescriptor.m_Parameters.m_NormMethodType == NormalizationAlgorithmMethod::LocalBrightness));
1452  CHECK(queueDescriptor.m_Parameters.m_NormSize == 3);
1453  CHECK(queueDescriptor.m_Parameters.m_Alpha == 0.5f);
1454  CHECK(queueDescriptor.m_Parameters.m_Beta == -1.0f);
1455  CHECK(queueDescriptor.m_Parameters.m_K == 0.2f);
1456  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
1457 
1458  CHECK(queueDescriptor.m_Inputs.size() == 1);
1459  CHECK(queueDescriptor.m_Outputs.size() == 1);
1460 
1461  // Returns so we can do extra, backend-specific tests.
1462  return workload;
1463 }
1464 
1465 template <typename Pooling2dWorkload, armnn::DataType DataType>
1466 std::unique_ptr<Pooling2dWorkload> CreatePooling2dWorkloadTest(armnn::IWorkloadFactory& factory,
1467  armnn::Graph& graph,
1468  DataLayout dataLayout = DataLayout::NCHW)
1469 {
1470  // Creates the layer we're testing.
1471  Pooling2dDescriptor layerDesc;
1472  layerDesc.m_PoolType = PoolingAlgorithm::Average;
1473  layerDesc.m_PoolWidth = 3;
1474  layerDesc.m_PoolHeight = 3;
1475  layerDesc.m_PadLeft = 2;
1476  layerDesc.m_PadRight = 2;
1477  layerDesc.m_PadTop = 1;
1478  layerDesc.m_PadBottom = 1;
1479  layerDesc.m_StrideX = 2;
1480  layerDesc.m_StrideY = 3;
1481  layerDesc.m_OutputShapeRounding = OutputShapeRounding::Floor;
1482  layerDesc.m_DataLayout = dataLayout;
1483 
1484  Pooling2dLayer* const layer = graph.AddLayer<Pooling2dLayer>(layerDesc, "layer");
1485 
1486  // Create extra layers
1487  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1488  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1489 
1490  TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 5, 5} : TensorShape{3, 5, 5, 2};
1491  TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 2, 4} : TensorShape{3, 2, 4, 2};
1492 
1493  // Connect up
1494  Connect(input, layer, TensorInfo(inputShape, DataType));
1495  Connect(layer, output, TensorInfo(outputShape, DataType));
1496  CreateTensorHandles(graph, factory);
1497 
1498  // Make the workload and checks it
1499  auto workload = MakeAndCheckWorkload<Pooling2dWorkload>(*layer, factory);
1500 
1501  Pooling2dQueueDescriptor queueDescriptor = workload->GetData();
1502  CHECK((queueDescriptor.m_Parameters.m_PoolType == PoolingAlgorithm::Average));
1503  CHECK((queueDescriptor.m_Parameters.m_OutputShapeRounding == OutputShapeRounding::Floor));
1504  CHECK(queueDescriptor.m_Parameters.m_PoolWidth == 3);
1505  CHECK(queueDescriptor.m_Parameters.m_PoolHeight == 3);
1506  CHECK(queueDescriptor.m_Parameters.m_StrideX == 2);
1507  CHECK(queueDescriptor.m_Parameters.m_StrideY == 3);
1508  CHECK(queueDescriptor.m_Parameters.m_PadLeft == 2);
1509  CHECK(queueDescriptor.m_Parameters.m_PadRight == 2);
1510  CHECK(queueDescriptor.m_Parameters.m_PadTop == 1);
1511  CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1);
1512  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
1513 
1514  CHECK(queueDescriptor.m_Inputs.size() == 1);
1515  CHECK(queueDescriptor.m_Outputs.size() == 1);
1516 
1517  // Return so we can do extra, backend-specific tests
1518  return workload;
1519 }
1520 
1521 template <typename SoftmaxWorkload, armnn::DataType DataType>
1522 std::unique_ptr<SoftmaxWorkload> CreateSoftmaxWorkloadTest(armnn::IWorkloadFactory& factory,
1523  armnn::Graph& graph)
1524 {
1525  // Create the layer we're testing.
1526  SoftmaxDescriptor softmaxDescriptor;
1527  // Set Axis to -1 if CL or Neon until further Axes are supported.
1528  if (factory.GetBackendId() == armnn::Compute::CpuAcc || factory.GetBackendId() == armnn::Compute::GpuAcc)
1529  {
1530  softmaxDescriptor.m_Axis = -1;
1531  }
1532 
1533  Layer* const layer = graph.AddLayer<SoftmaxLayer>(softmaxDescriptor, "layer");
1534  // Create extra layers.
1535  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1536  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1537 
1538  // Connect up
1539  armnn::TensorInfo tensorInfo({4, 1}, DataType);
1540  if (DataType == armnn::DataType::QAsymmU8)
1541  {
1542  tensorInfo.SetQuantizationOffset(0);
1543  tensorInfo.SetQuantizationScale(1.f / 256);
1544  }
1545  else if (DataType == armnn::DataType::QAsymmS8)
1546  {
1547  tensorInfo.SetQuantizationOffset(-128);
1548  tensorInfo.SetQuantizationScale(1.f / 256);
1549  }
1550 
1551  Connect(input, layer, tensorInfo);
1552  Connect(layer, output, tensorInfo);
1553  CreateTensorHandles(graph, factory);
1554 
1555  // Make the workload and checks it.
1556  auto workload = MakeAndCheckWorkload<SoftmaxWorkload>(*layer, factory);
1557 
1558  SoftmaxQueueDescriptor queueDescriptor = workload->GetData();
1559  CHECK(queueDescriptor.m_Inputs.size() == 1);
1560  CHECK(queueDescriptor.m_Outputs.size() == 1);
1561 
1562  // Return so we can do extra, backend-specific tests.
1563  return workload;
1564 }
1565 
1566 template<typename SplitterWorkload, armnn::DataType DataType>
1567 std::unique_ptr<SplitterWorkload>
1568  CreateSplitterWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph)
1569 {
1570  // Create the layer we're testing.
1571  // NOTE: need three dimensions channels, height/y, width/x because the Compute
1572  // library restricts subtensors to have the same x and y dimensions as
1573  // their parent tensors, and therefore the origin on the x and y dimension
1574  // has to be zero for any view. So we need a third dimension to split...
1575  // NOTE: arguments are: number of views, number of dimensions.
1576  ViewsDescriptor layerDesc(3, 3);
1577  // NOTE: arguments are: view, dimension, value.
1578  layerDesc.SetViewOriginCoord(0, 0, 0);
1579  layerDesc.SetViewOriginCoord(1, 0, 1);
1580  layerDesc.SetViewOriginCoord(2, 0, 3);
1581 
1582  Layer* const layer = graph.AddLayer<SplitterLayer>(layerDesc, "layer");
1583 
1584  // Adds extra layers.
1585  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1586  Layer* const output0 = graph.AddLayer<OutputLayer>(0, "output0");
1587  Layer* const output1 = graph.AddLayer<OutputLayer>(1, "output1");
1588  Layer* const output2 = graph.AddLayer<OutputLayer>(2, "output2");
1589 
1590  // Connects up.
1591  armnn::TensorInfo tensorInfo({5, 7, 7}, DataType);
1592  Connect(input, layer, tensorInfo);
1593 
1594  armnn::TensorInfo output0Info({1, 7, 7}, DataType);
1595  armnn::TensorInfo output1Info({2, 7, 7}, DataType);
1596  armnn::TensorInfo output2Info({2, 7, 7}, DataType);
1597 
1598  Connect(layer, output0, output0Info, 0, 0);
1599  Connect(layer, output1, output1Info, 1, 0);
1600  Connect(layer, output2, output2Info, 2, 0);
1601 
1602  CreateTensorHandles(graph, factory);
1603 
1604  // Makes the workload and checks it.
1605  auto workload = MakeAndCheckWorkload<SplitterWorkload>(*layer, factory);
1606 
1607  SplitterQueueDescriptor queueDescriptor = workload->GetData();
1608  CHECK(queueDescriptor.m_Inputs.size() == 1);
1609  CHECK(queueDescriptor.m_Outputs.size() == 3);
1610  CHECK(queueDescriptor.m_ViewOrigins.size() == 3);
1611 
1612  CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[0] == 0);
1613  CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[0] == 1);
1614  CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[0] == 3);
1615  CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[1] == 0);
1616  CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[1] == 0);
1617  CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[1] == 0);
1618  CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[2] == 0);
1619  CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[2] == 0);
1620  CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[2] == 0);
1621 
1622  // Returns so we can do extra, backend-specific tests.
1623  return workload;
1624 }
1625 
1626 /// This function constructs a graph with both a splitter and a concat, and returns a pair of the workloads.
1627 template<typename SplitterWorkload, typename ConcatWorkload, armnn::DataType DataType>
1628 std::pair<std::unique_ptr<SplitterWorkload>, std::unique_ptr<ConcatWorkload>>
1629  CreateSplitterConcatWorkloadTest(armnn::IWorkloadFactory &factory, armnn::Graph &graph)
1630 {
1631  armnn::TensorInfo inputTensorInfo({ 1, 2, 100, 10 }, DataType);
1632 
1633  armnn::TensorInfo splitTensorInfo1({ 1, 1, 100, 10 }, DataType);
1634  armnn::TensorInfo splitTensorInfo2({ 1, 1, 100, 10 }, DataType);
1635 
1636  //Constructs the graph.
1637  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1638 
1639  armnn::ViewsDescriptor splitterViews(2);
1640  splitterViews.SetViewOriginCoord(0, 0, 0);
1641  splitterViews.SetViewOriginCoord(0, 1, 0);
1642  splitterViews.SetViewOriginCoord(0, 2, 0);
1643  splitterViews.SetViewOriginCoord(0, 3, 0);
1644 
1645  splitterViews.SetViewOriginCoord(1, 0, 0);
1646  splitterViews.SetViewOriginCoord(1, 1, 1);
1647  splitterViews.SetViewOriginCoord(1, 2, 0);
1648  splitterViews.SetViewOriginCoord(1, 3, 0);
1649 
1650  // create splitter layer
1651  Layer* const splitter = graph.AddLayer<SplitterLayer>(splitterViews, "splitter");
1652  CHECK(splitter);
1653 
1654  armnn::OriginsDescriptor concatViews(2);
1655  concatViews.SetViewOriginCoord(0, 0, 0);
1656  concatViews.SetViewOriginCoord(0, 1, 1);
1657  concatViews.SetViewOriginCoord(0, 2, 0);
1658  concatViews.SetViewOriginCoord(0, 3, 0);
1659 
1660  concatViews.SetViewOriginCoord(1, 0, 0);
1661  concatViews.SetViewOriginCoord(1, 1, 0);
1662  concatViews.SetViewOriginCoord(1, 2, 0);
1663  concatViews.SetViewOriginCoord(1, 3, 0);
1664 
1665  // create concat layer
1666  Layer* const concat = graph.AddLayer<ConcatLayer>(concatViews, "concat");
1667  CHECK(concat);
1668 
1669  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1670 
1671  // Adds connections.
1672  // connect input to splitter
1673  Connect(input, splitter, inputTensorInfo, 0, 0);
1674  // connect splitter[0] to concat[1]
1675  Connect(splitter, concat, splitTensorInfo1, 0, 1); // The splitter & concat are connected up.
1676  // connect splitter[1] to concat[0]
1677  Connect(splitter, concat, splitTensorInfo2, 1, 0); // So that the outputs are flipped round.
1678  // connect concat to output
1679  Connect(concat, output, inputTensorInfo, 0, 0);
1680 
1681  // created tensor handles
1682  CreateTensorHandles(graph, factory);
1683 
1684  // created splitter workload
1685  auto workloadSplitter = MakeAndCheckWorkload<SplitterWorkload>(*splitter, factory);
1686  CHECK(workloadSplitter);
1687  // created concat workload
1688  auto workloadConcat = MakeAndCheckWorkload<ConcatWorkload>(*concat, factory);
1689  CHECK(workloadConcat);
1690 
1691  return {std::move(workloadSplitter), std::move(workloadConcat)};
1692 }
1693 
1694 
1695 /// This function constructs a graph with a splitter with two outputs. Each of the outputs is then
1696 /// connected to two different activation layers
1697 template<typename SplitterWorkload, typename ActivationWorkload, armnn::DataType DataType>
1698 void CreateSplitterMultipleInputsOneOutputWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph,
1699  std::unique_ptr<SplitterWorkload>& wlSplitter,
1700  std::unique_ptr<ActivationWorkload>& wlActiv0_0,
1701  std::unique_ptr<ActivationWorkload>& wlActiv0_1,
1702  std::unique_ptr<ActivationWorkload>& wlActiv1_0,
1703  std::unique_ptr<ActivationWorkload>& wlActiv1_1)
1704 {
1705  armnn::TensorInfo inputTensorInfo ({ 1, 3, 100, 50 }, DataType);
1706  armnn::TensorInfo splitTensorInfo1({ 1, 1, 100, 50 }, DataType);
1707  armnn::TensorInfo splitTensorInfo2({ 1, 2, 100, 50 }, DataType);
1708 
1709  //Constructs the graph.
1710  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1711 
1712  armnn::ViewsDescriptor splitterViews(2);
1713 
1714  splitterViews.SetViewOriginCoord(0, 0, 0);
1715  splitterViews.SetViewOriginCoord(0, 1, 0);
1716  splitterViews.SetViewOriginCoord(0, 2, 0);
1717  splitterViews.SetViewOriginCoord(0, 3, 0);
1718 
1719  splitterViews.SetViewOriginCoord(1, 0, 0);
1720  splitterViews.SetViewOriginCoord(1, 1, 1);
1721  splitterViews.SetViewOriginCoord(1, 2, 0);
1722  splitterViews.SetViewOriginCoord(1, 3, 0);
1723 
1724  Layer* const splitter = graph.AddLayer<SplitterLayer>(splitterViews, "splitter");
1725 
1726  armnn::ActivationDescriptor activationDesc;
1727 
1728  Layer* const activ0_0 = graph.AddLayer<ActivationLayer>(activationDesc, "activ0_0");
1729  Layer* const activ0_1 = graph.AddLayer<ActivationLayer>(activationDesc, "activ0_1");
1730  Layer* const activ1_0 = graph.AddLayer<ActivationLayer>(activationDesc, "activ1_0");
1731  Layer* const activ1_1 = graph.AddLayer<ActivationLayer>(activationDesc, "activ1_1");
1732 
1733  Layer* const output1 = graph.AddLayer<OutputLayer>(1, "output1");
1734  Layer* const output2 = graph.AddLayer<OutputLayer>(2, "output2");
1735  Layer* const output3 = graph.AddLayer<OutputLayer>(3, "output3");
1736  Layer* const output4 = graph.AddLayer<OutputLayer>(4, "output4");
1737 
1738  // Adds connections.
1739  Connect(input, splitter, inputTensorInfo, 0, 0);
1740  Connect(splitter, activ0_0, splitTensorInfo1, 0, 0);
1741  Connect(splitter, activ0_1, splitTensorInfo1, 0, 0);
1742 
1743  Connect(splitter, activ1_0, splitTensorInfo2, 1, 0);
1744  Connect(splitter, activ1_1, splitTensorInfo2, 1, 0);
1745 
1746  Connect(activ0_0, output1, splitTensorInfo1, 0, 0);
1747  Connect(activ0_1, output2, splitTensorInfo1, 0, 0);
1748  Connect(activ1_0, output3, splitTensorInfo2, 0, 0);
1749  Connect(activ1_1, output4, splitTensorInfo2, 0, 0);
1750 
1751  CreateTensorHandles(graph, factory);
1752 
1753  auto workloadSplitter = MakeAndCheckWorkload<SplitterWorkload>(*splitter, factory);
1754  auto workloadActiv0_0 = MakeAndCheckWorkload<ActivationWorkload>(*activ0_0, factory);
1755  auto workloadActiv0_1 = MakeAndCheckWorkload<ActivationWorkload>(*activ0_1, factory);
1756  auto workloadActiv1_0 = MakeAndCheckWorkload<ActivationWorkload>(*activ1_0, factory);
1757  auto workloadActiv1_1 = MakeAndCheckWorkload<ActivationWorkload>(*activ1_1, factory);
1758 
1759  wlSplitter = std::move(workloadSplitter);
1760  wlActiv0_0 = std::move(workloadActiv0_0);
1761  wlActiv0_1 = std::move(workloadActiv0_1);
1762  wlActiv1_0 = std::move(workloadActiv1_0);
1763  wlActiv1_1 = std::move(workloadActiv1_1);
1764 }
1765 
1766 template <typename ResizeWorkload, armnn::DataType DataType>
1767 std::unique_ptr<ResizeWorkload> CreateResizeBilinearWorkloadTest(armnn::IWorkloadFactory& factory,
1768  armnn::Graph& graph,
1769  DataLayout dataLayout = DataLayout::NCHW)
1770 {
1771  TensorShape inputShape;
1772  TensorShape outputShape;
1773 
1774  switch (dataLayout) {
1775  case DataLayout::NHWC:
1776  inputShape = { 2, 4, 4, 3 };
1777  outputShape = { 2, 2, 2, 3 };
1778  break;
1779  case DataLayout::NCHW:
1780  default:
1781  inputShape = { 2, 3, 4, 4 };
1782  outputShape = { 2, 3, 2, 2 };
1783  }
1784 
1785  // Creates the layer we're testing.
1786  ResizeDescriptor resizeDesc;
1787  armnnUtils::DataLayoutIndexed dimensionIndices = dataLayout;
1788  resizeDesc.m_Method = ResizeMethod::Bilinear;
1789  resizeDesc.m_TargetWidth = outputShape[dimensionIndices.GetWidthIndex()];
1790  resizeDesc.m_TargetHeight = outputShape[dimensionIndices.GetHeightIndex()];
1791  resizeDesc.m_DataLayout = dataLayout;
1792  Layer* const layer = graph.AddLayer<ResizeLayer>(resizeDesc, "resize");
1793 
1794  // Creates extra layers.
1795  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1796  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1797 
1798  // Connects up.
1799  armnn::TensorInfo inputTensorInfo(inputShape, DataType);
1800  armnn::TensorInfo outputTensorInfo(outputShape, DataType);
1801  Connect(input, layer, inputTensorInfo);
1802  Connect(layer, output, outputTensorInfo);
1803  CreateTensorHandles(graph, factory);
1804 
1805  // Makes the workload and checks it.
1806  auto workload = MakeAndCheckWorkload<ResizeWorkload>(*layer, factory);
1807 
1808  auto queueDescriptor = workload->GetData();
1809  CHECK(queueDescriptor.m_Inputs.size() == 1);
1810  CHECK(queueDescriptor.m_Outputs.size() == 1);
1811  CHECK(queueDescriptor.m_Parameters.m_DataLayout == dataLayout);
1812 
1813  // Returns so we can do extra, backend-specific tests.
1814  return workload;
1815 }
1816 
1817 template <typename BatchToSpaceNdWorkload, armnn::DataType DataType>
1818 std::unique_ptr<BatchToSpaceNdWorkload> CreateBatchToSpaceNdWorkloadTest(armnn::IWorkloadFactory& factory,
1819  armnn::Graph& graph)
1820 {
1821  BatchToSpaceNdDescriptor desc;
1822  Layer* const layer = graph.AddLayer<BatchToSpaceNdLayer>(desc, "batchToSpace");
1823 
1824  // Creates extra layers.
1825  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1826  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1827 
1828  // Connects up.
1829  armnn::TensorInfo tensorInfo({1, 1, 1, 1}, DataType);
1830 
1831  Connect(input, layer, tensorInfo);
1832  Connect(layer, output, tensorInfo);
1833 
1834  CreateTensorHandles(graph, factory);
1835 
1836  // Makes the workload and checks it.
1837  auto workload = MakeAndCheckWorkload<BatchToSpaceNdWorkload>(*layer, factory);
1838 
1839  BatchToSpaceNdQueueDescriptor queueDescriptor = workload->GetData();
1840  CHECK(queueDescriptor.m_Inputs.size() == 1);
1841  CHECK(queueDescriptor.m_Outputs.size() == 1);
1842 
1843  return workload;
1844 }
1845 
1846 template <typename LogSoftmaxWorkload, armnn::DataType DataType>
1847 std::unique_ptr<LogSoftmaxWorkload> CreateLogSoftmaxWorkloadTest(armnn::IWorkloadFactory& factory,
1848  armnn::Graph& graph)
1849 {
1850  // Create the layer we're testing.
1851  LogSoftmaxDescriptor logSoftmaxDescriptor;
1852  // Set Axis to -1 if CL or Neon until further Axes are supported.
1853  if (factory.GetBackendId() == armnn::Compute::CpuAcc || factory.GetBackendId() == armnn::Compute::GpuAcc)
1854  {
1855  logSoftmaxDescriptor.m_Axis = -1;
1856  }
1857 
1858  Layer* const layer = graph.AddLayer<LogSoftmaxLayer>(logSoftmaxDescriptor, "layer");
1859  // Create extra layers.
1860  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1861  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1862 
1863  // Connect up
1864  armnn::TensorInfo tensorInfo({4, 1}, DataType);
1865 
1866  Connect(input, layer, tensorInfo);
1867  Connect(layer, output, tensorInfo);
1868  CreateTensorHandles(graph, factory);
1869 
1870  // Make the workload and checks it.
1871  auto workload = MakeAndCheckWorkload<LogSoftmaxWorkload>(*layer, factory);
1872 
1873  LogSoftmaxQueueDescriptor queueDescriptor = workload->GetData();
1874  CHECK(queueDescriptor.m_Inputs.size() == 1);
1875  CHECK(queueDescriptor.m_Outputs.size() == 1);
1876 
1877  // Return so we can do extra, backend-specific tests.
1878  return workload;
1879 }
1880 
1881 template <typename L2NormalizationWorkload, armnn::DataType DataType>
1882 std::unique_ptr<L2NormalizationWorkload> CreateL2NormalizationWorkloadTest(armnn::IWorkloadFactory& factory,
1883  armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW)
1884 {
1885  // Creates the layer we're testing.
1886  L2NormalizationDescriptor layerDesc;
1887  layerDesc.m_DataLayout = dataLayout;
1888 
1889  Layer* const layer = graph.AddLayer<L2NormalizationLayer>(layerDesc, "l2norm");
1890 
1891  // Creates extra layers.
1892  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1893  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1894 
1895  TensorShape inputShape = (dataLayout == DataLayout::NCHW) ?
1896  TensorShape{ 5, 20, 50, 67 } : TensorShape{ 5, 50, 67, 20 };
1897  TensorShape outputShape = (dataLayout == DataLayout::NCHW) ?
1898  TensorShape{ 5, 20, 50, 67 } : TensorShape{ 5, 50, 67, 20 };
1899 
1900  // Connects up.
1901  armnn::TensorInfo inputTensorInfo(inputShape, DataType);
1902  armnn::TensorInfo outputTensorInfo(outputShape, DataType);
1903  Connect(input, layer, inputTensorInfo);
1904  Connect(layer, output, outputTensorInfo);
1905  CreateTensorHandles(graph, factory);
1906 
1907  // Makes the workload and checks it.
1908  auto workload = MakeAndCheckWorkload<L2NormalizationWorkload>(*layer, factory);
1909 
1910  L2NormalizationQueueDescriptor queueDescriptor = workload->GetData();
1911  CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
1912  CHECK(queueDescriptor.m_Inputs.size() == 1);
1913  CHECK(queueDescriptor.m_Outputs.size() == 1);
1914 
1915  // Returns so we can do extra, backend-specific tests.
1916  return workload;
1917 }
1918 
1919 template <typename ReshapeWorkload, armnn::DataType DataType>
1920 std::unique_ptr<ReshapeWorkload> CreateReshapeWorkloadTest(armnn::IWorkloadFactory& factory,
1921  armnn::Graph& graph)
1922 {
1923  // Creates the layer we're testing.
1924  TensorShape outputShape({ 1, 4 });
1925  ReshapeDescriptor reshapeDesc;
1926  reshapeDesc.m_TargetShape = outputShape;
1927  Layer* const layer = graph.AddLayer<ReshapeLayer>(reshapeDesc, "layer");
1928 
1929  // Creates extra layers.
1930  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1931  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1932 
1933  // Connects up.
1934  armnn::TensorInfo inputTensorInfo({ 4, 1 }, DataType);
1935  armnn::TensorInfo outputTensorInfo(outputShape, DataType);
1936  Connect(input, layer, inputTensorInfo);
1937  Connect(layer, output, outputTensorInfo);
1938  CreateTensorHandles(graph, factory);
1939 
1940  // Makes the workload and checks it.
1941  auto workload = MakeAndCheckWorkload<ReshapeWorkload>(*layer, factory);
1942 
1943  ReshapeQueueDescriptor queueDescriptor = workload->GetData();
1944  CHECK(queueDescriptor.m_Inputs.size() == 1);
1945  CHECK(queueDescriptor.m_Outputs.size() == 1);
1946 
1947  // Returns so we can do extra, backend-specific tests.
1948  return workload;
1949 }
1950 
1951 template <typename ConvertFp16ToFp32Float32Workload>
1952 std::unique_ptr<ConvertFp16ToFp32Float32Workload> CreateConvertFp16ToFp32WorkloadTest(
1953  armnn::IWorkloadFactory& factory, armnn::Graph& graph)
1954 {
1955  // Creates the layer we're testing.
1956  ConvertFp16ToFp32Layer* const layer = graph.AddLayer<ConvertFp16ToFp32Layer>("Fp16ToFp32Converter");
1957 
1958  // Creates extra layers.
1959  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1960  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1961 
1962  // Connects up.
1963  armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16);
1964  armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32);
1965  Connect(input, layer, inputTensorInfo);
1966  Connect(layer, output, outputTensorInfo);
1967  CreateTensorHandles(graph, factory);
1968 
1969  // Makes the workload and checks it.
1970  auto workload = MakeAndCheckWorkload<ConvertFp16ToFp32Float32Workload>(*layer, factory);
1971 
1972  ConvertFp16ToFp32QueueDescriptor queueDescriptor = workload->GetData();
1973  CHECK(queueDescriptor.m_Inputs.size() == 1);
1974  CHECK(queueDescriptor.m_Outputs.size() == 1);
1975 
1976  // Returns so we can do extra, backend-specific tests.
1977  return workload;
1978 }
1979 
1980 template <typename ConvertFp32ToFp16Float16Workload>
1981 std::unique_ptr<ConvertFp32ToFp16Float16Workload> CreateConvertFp32ToFp16WorkloadTest(
1982  armnn::IWorkloadFactory& factory, armnn::Graph& graph)
1983 {
1984  // Creates the layer we're testing.
1985  ConvertFp32ToFp16Layer* const layer = graph.AddLayer<ConvertFp32ToFp16Layer>("Fp32ToFp16Converter");
1986 
1987  // Creates extra layers.
1988  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
1989  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
1990 
1991  // Connects up.
1992  armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32);
1993  armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16);
1994  Connect(input, layer, inputTensorInfo);
1995  Connect(layer, output, outputTensorInfo);
1996  CreateTensorHandles(graph, factory);
1997 
1998  // Makes the workload and checks it.
1999  auto workload = MakeAndCheckWorkload<ConvertFp32ToFp16Float16Workload>(*layer, factory);
2000 
2001  ConvertFp32ToFp16QueueDescriptor queueDescriptor = workload->GetData();
2002  CHECK(queueDescriptor.m_Inputs.size() == 1);
2003  CHECK(queueDescriptor.m_Outputs.size() == 1);
2004 
2005  // Returns so we can do extra, backend-specific tests.
2006  return workload;
2007 }
2008 
2009 template <typename MeanWorkload, armnn::DataType DataType>
2010 std::unique_ptr<MeanWorkload> CreateMeanWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph)
2011 {
2012  // Reduce along the first and second dimensions, and do not keep the reduced dimensions.
2013  MeanDescriptor descriptor({ 1, 2 }, false);
2014 
2015  // Creates the layer we're testing.
2016  Layer* const layer = graph.AddLayer<MeanLayer>(descriptor, "mean");
2017 
2018  // Creates extra layers.
2019  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
2020  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
2021 
2022  // Connects up.
2023  armnn::TensorInfo inputTensorInfo({ 1, 3, 7, 4 }, DataType);
2024  armnn::TensorInfo outputTensorInfo({ 1, 4 }, DataType);
2025  Connect(input, layer, inputTensorInfo);
2026  Connect(layer, output, outputTensorInfo);
2027  CreateTensorHandles(graph, factory);
2028 
2029  // Makes the workload and checks it.
2030  auto workload = MakeAndCheckWorkload<MeanWorkload>(*layer, factory);
2031 
2032  MeanQueueDescriptor queueDescriptor = workload->GetData();
2033  CHECK(queueDescriptor.m_Parameters.m_Axis == descriptor.m_Axis);
2034  CHECK(queueDescriptor.m_Parameters.m_KeepDims == descriptor.m_KeepDims);
2035  CHECK(queueDescriptor.m_Inputs.size() == 1);
2036  CHECK(queueDescriptor.m_Outputs.size() == 1);
2037 
2038  // Returns so we can do extra, backend-specific tests.
2039  return workload;
2040 }
2041 
2042 template<typename ConcatWorkload, armnn::DataType DataType>
2043 std::unique_ptr<ConcatWorkload> CreateConcatWorkloadTest(armnn::IWorkloadFactory &factory,
2044  armnn::Graph &graph,
2045  const armnn::TensorShape &outputShape,
2046  unsigned int concatAxis)
2047 {
2048  armnn::TensorInfo inputTensorInfo({ 2, 3, 2, 5 }, DataType);
2049  armnn::TensorInfo outputTensorInfo(outputShape, DataType);
2050 
2051  // Constructs the graph.
2052  Layer* const input0 = graph.AddLayer<InputLayer>(0, "input0");
2053  Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1");
2054  armnn::OriginsDescriptor descriptor;
2055 
2056  std::vector<armnn::TensorShape> inputShapes{{ 2, 3, 2, 5 }, { 2, 3, 2, 5 }};
2057 
2058  descriptor = CreateDescriptorForConcatenation(inputShapes.begin(),
2059  inputShapes.end(),
2060  concatAxis);
2061 
2062  // create concat layer
2063  Layer* const concat = graph.AddLayer<ConcatLayer>(descriptor, "concat");
2064  CHECK(concat);
2065 
2066  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
2067 
2068  // Adds connections.
2069  // connect input0 to concat
2070  Connect(input0, concat, inputTensorInfo, 0, 0);
2071  // connect input1 to concat
2072  Connect(input1, concat, inputTensorInfo, 0, 1);
2073  // connect concat to output
2074  Connect(concat, output, outputTensorInfo, 0, 0);
2075 
2076  // create tensor handles
2077  CreateTensorHandles(graph, factory);
2078 
2079  // create concat workload
2080  auto workloadConcat = MakeAndCheckWorkload<ConcatWorkload>(*concat, factory);
2081  CHECK(workloadConcat);
2082 
2083  return workloadConcat;
2084 }
2085 
2086 template <typename PreCompiledWorkload, armnn::DataType dataType>
2087 std::pair<armnn::IOptimizedNetworkPtr, std::unique_ptr<PreCompiledWorkload>> CreatePreCompiledWorkloadTest(
2088  armnn::IWorkloadFactory& factory,
2089  armnn::Graph& graph,
2090  bool biasEnabled = false)
2091 {
2092  IgnoreUnused(graph);
2093 
2094  // build up the structure of the network
2095  armnn::INetworkPtr net(armnn::INetwork::Create());
2096 
2097  // Add an input layer
2098  armnn::IConnectableLayer* const inputLayer = net->AddInputLayer(0, "input layer");
2099  CHECK(inputLayer);
2100 
2101  // ArmNN weights tensor shape is OIHW (out channels, in channels, height, width) for NCHW
2102  // ArmNN weights tensor shape is OHWI (out channels, height, width, in channels) for NHWC
2103  // this test is using NHWC, so the weights shape is OHWI
2104  TensorInfo weightsTensorInfo(TensorShape({16, 1, 1, 16}), dataType, 0.9f, 0, true);
2105  unsigned int weightsLength = weightsTensorInfo.GetNumElements();
2106 
2107  using WeightType = armnn::ResolveType<dataType>;
2108  std::vector<WeightType> convWeightsData(weightsLength);
2109  for (unsigned int i = 0; i < weightsLength; ++i)
2110  {
2111  convWeightsData[i] = static_cast<WeightType>(i);
2112  }
2113 
2114  armnn::ConstTensor weights(weightsTensorInfo, convWeightsData);
2115 
2116  // Add a layer that can be used in the PreCompiled layer
2117  armnn::Convolution2dDescriptor convDesc2d;
2118  convDesc2d.m_StrideX = 1;
2119  convDesc2d.m_StrideY = 1;
2120  convDesc2d.m_BiasEnabled = biasEnabled;
2121  convDesc2d.m_DataLayout = armnn::DataLayout::NHWC;
2122 
2123 
2124  const std::string convLayerName("conv layer");
2125 
2126  armnn::IConnectableLayer* convLayer = net->AddConvolution2dLayer(convDesc2d, convLayerName.c_str());
2127 
2128  IConnectableLayer* weightsLayer = net->AddConstantLayer(weights);
2129  weightsLayer->GetOutputSlot(0).SetTensorInfo(weights.GetInfo());
2130  weightsLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(1u));
2131 
2132  if (biasEnabled)
2133  {
2134  constexpr armnn::DataType biasDataType = ( dataType == armnn::DataType::QAsymmU8) ?
2135  armnn::DataType::Signed32 : armnn::DataType::Float32;
2136 
2137  TensorInfo biasTensorInfo(TensorShape({16}), biasDataType, 0.9f * 0.9f, 0, true);
2138  unsigned int biasLength = biasTensorInfo.GetNumElements();
2139 
2140  using BiasType = armnn::ResolveType<biasDataType>;
2141  std::vector<BiasType> biasData(biasLength);
2142  std::fill(biasData.begin(), biasData.end(), static_cast<BiasType>(0));
2143 
2144  armnn::ConstTensor biases(biasTensorInfo, biasData);
2145 
2146  IConnectableLayer* biasLayer = net->AddConstantLayer(biases);
2147 
2148  biasLayer->GetOutputSlot(0).SetTensorInfo(biases.GetInfo());
2149  biasLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(2u));
2150  }
2151 
2152  CHECK(convLayer);
2153 
2154  // Add an output layer
2155  armnn::IConnectableLayer* const outputLayer = net->AddOutputLayer(0, "output layer");
2156  CHECK(outputLayer);
2157 
2158  // set the tensors in the network (NHWC format)
2159  TensorInfo inputTensorInfo(TensorShape({ 1, 16, 16, 16 }), dataType);
2160  if (dataType == armnn::DataType::QAsymmU8)
2161  {
2162  inputTensorInfo.SetQuantizationOffset(0);
2163  inputTensorInfo.SetQuantizationScale(0.9f);
2164  }
2165 
2166  TensorInfo outputTensorInfo(TensorShape({1, 16, 16, 16}), dataType);
2167  if (dataType == armnn::DataType::QAsymmU8)
2168  {
2169  outputTensorInfo.SetQuantizationOffset(0);
2170  outputTensorInfo.SetQuantizationScale(0.9f);
2171  }
2172 
2173  // Connect the layers
2174  inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
2175  inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
2176 
2177  convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
2178  convLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
2179 
2180  // Optimize the network for the backend supported by the factory
2181  std::vector<armnn::BackendId> backends = {factory.GetBackendId()};
2182  armnn::IRuntime::CreationOptions options;
2183  armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
2184  armnn::OptimizerOptionsOpaque optimizerOptions;
2185  armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec(),
2186  optimizerOptions);
2187  CHECK(optimizedNet != nullptr);
2188 
2189  // Find the PreCompiled layer in the optimised graph
2190  armnn::Graph& optimisedGraph = GetGraphForTesting(optimizedNet.get());
2191  Layer* preCompiledLayer = nullptr;
2192  for (auto& layer : optimisedGraph)
2193  {
2194  if (layer->GetType() == LayerType::PreCompiled)
2195  {
2196  preCompiledLayer = layer;
2197  }
2198  }
2199  CHECK(preCompiledLayer != nullptr);
2200 
2201  // Create the TensorHandles.
2202  CreateTensorHandles(optimisedGraph, factory);
2203 
2204  // Make the workload and check it.
2205  auto workload = MakeAndCheckWorkload<PreCompiledWorkload>(*preCompiledLayer, factory);
2206 
2207  PreCompiledQueueDescriptor queueDescriptor = workload->GetData();
2208  CHECK(queueDescriptor.m_Inputs.size() == 1);
2209  CHECK(queueDescriptor.m_Outputs.size() == 1);
2210 
2211  // Returns the workload so we can do extra, backend-specific tests.
2212  // NOTE: We need to return the optimised network as well, otherwise it gets
2213  // out of scope and the tensor handles get destructed
2214  return std::make_pair(std::move(optimizedNet), std::move(workload));
2215 }
2216 
2217 template<typename ConstantWorkload, armnn::DataType DataType>
2218 std::unique_ptr<ConstantWorkload> CreateConstantWorkloadTest(armnn::IWorkloadFactory& factory,
2219  armnn::Graph& graph,
2220  const armnn::TensorShape& outputShape)
2221 {
2222  armnn::TensorInfo outputTensorInfo(outputShape, DataType);
2223 
2224  // create constant layer
2225  auto constant = graph.AddLayer<ConstantLayer>("constant");
2226  CHECK(constant);
2227  constant->m_LayerOutput = std::make_unique<ScopedTensorHandle>(outputTensorInfo);
2228 
2229  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
2230 
2231  // Adds connections.
2232  // connect constant to output
2233  Connect(constant, output, outputTensorInfo, 0, 0);
2234 
2235  // create tensor handles
2236  CreateTensorHandles(graph, factory);
2237 
2238  // create Constant workload"
2239  auto workloadConstant = MakeAndCheckWorkload<ConstantWorkload>(*constant, factory);
2240  CHECK(workloadConstant);
2241 
2242  return workloadConstant;
2243 }
2244 
2245 template <typename PreluWorkload>
2246 std::unique_ptr<PreluWorkload> CreatePreluWorkloadTest(armnn::IWorkloadFactory& factory,
2247  armnn::Graph& graph,
2248  const armnn::TensorShape& inputShape,
2249  const armnn::TensorShape& alphaShape,
2250  const armnn::TensorShape& outputShape,
2251  armnn::DataType dataType)
2252 {
2253  // Creates the PReLU layer
2254  Layer* const layer = graph.AddLayer<PreluLayer>("prelu");
2255  CHECK(layer != nullptr);
2256 
2257  // Creates extra layers
2258  Layer* const input = graph.AddLayer<InputLayer> (0, "input");
2259  Layer* const alpha = graph.AddLayer<InputLayer> (1, "alpha");
2260  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
2261  CHECK(input != nullptr);
2262  CHECK(alpha != nullptr);
2263  CHECK(output != nullptr);
2264 
2265  // Connects up
2266  armnn::TensorInfo inputTensorInfo (inputShape, dataType);
2267  armnn::TensorInfo alphaTensorInfo (alphaShape, dataType);
2268  armnn::TensorInfo outputTensorInfo(outputShape, dataType);
2269  Connect(input, layer, inputTensorInfo, 0, 0);
2270  Connect(alpha, layer, alphaTensorInfo, 0, 1);
2271  Connect(layer, output, outputTensorInfo, 0, 0);
2272  CreateTensorHandles(graph, factory);
2273 
2274  // Makes the workload and checks it
2275  auto workload = MakeAndCheckWorkload<PreluWorkload>(*layer, factory);
2276 
2277  PreluQueueDescriptor queueDescriptor = workload->GetData();
2278  CHECK(queueDescriptor.m_Inputs.size() == 2);
2279  CHECK(queueDescriptor.m_Outputs.size() == 1);
2280 
2281  // Returns so we can do extra, backend-specific tests.
2282  return workload;
2283 }
2284 
2285 template <typename SpaceToDepthWorkload, armnn::DataType DataType>
2286 std::unique_ptr<SpaceToDepthWorkload> CreateSpaceToDepthWorkloadTest(armnn::IWorkloadFactory& factory,
2287  armnn::Graph& graph)
2288 {
2289  SpaceToDepthDescriptor desc;
2290  desc.m_BlockSize = 2;
2291  Layer* const layer = graph.AddLayer<SpaceToDepthLayer>(desc, "spaceToDepth");
2292 
2293  // Creates extra layers.
2294  Layer* const input = graph.AddLayer<InputLayer>(0, "input");
2295  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
2296 
2297  // Connects up.
2298  armnn::TensorInfo inputTensorInfo({ 1, 2, 2, 1 }, DataType);
2299  armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 4 }, DataType);
2300 
2301  Connect(input, layer, inputTensorInfo);
2302  Connect(layer, output, outputTensorInfo);
2303 
2304  CreateTensorHandles(graph, factory);
2305 
2306  // Makes the workload and checks it.
2307  auto workload = MakeAndCheckWorkload<SpaceToDepthWorkload>(*layer, factory);
2308 
2309  SpaceToDepthQueueDescriptor queueDescriptor = workload->GetData();
2310  CHECK(queueDescriptor.m_Inputs.size() == 1);
2311  CHECK(queueDescriptor.m_Outputs.size() == 1);
2312 
2313  return workload;
2314 }
2315 
2316 template <typename StackWorkload, armnn::DataType DataType>
2317 std::unique_ptr<StackWorkload> CreateStackWorkloadTest(armnn::IWorkloadFactory& factory,
2318  armnn::Graph& graph,
2319  const armnn::TensorShape& inputShape,
2320  const armnn::TensorShape& outputShape,
2321  unsigned int axis,
2322  unsigned int numInputs)
2323 {
2324  armnn::TensorInfo inputTensorInfo(inputShape, DataType);
2325  armnn::TensorInfo outputTensorInfo(outputShape, DataType);
2326 
2327  // Constructs the Stack layer.
2328  armnn::StackDescriptor descriptor(axis, numInputs, inputShape);
2329  Layer* const stackLayer = graph.AddLayer<StackLayer>(descriptor, "stack");
2330  CHECK(stackLayer != nullptr);
2331 
2332  // Constructs layer inputs and output.
2333  std::vector<Layer*> inputs;
2334  for (unsigned int i=0; i<numInputs; ++i)
2335  {
2336  inputs.push_back(graph.AddLayer<InputLayer>(
2337  static_cast<int>(i),
2338  ("input" + std::to_string(i)).c_str()
2339  ));
2340  CHECK(inputs[i] != nullptr);
2341  }
2342  Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
2343  CHECK(output != nullptr);
2344 
2345  // Adds connections.
2346  for (unsigned int i=0; i<numInputs; ++i)
2347  {
2348  Connect(inputs[i], stackLayer, inputTensorInfo, 0, i);
2349  }
2350  Connect(stackLayer, output, outputTensorInfo, 0, 0);
2351 
2352  CreateTensorHandles(graph, factory);
2353 
2354  auto stackWorkload = MakeAndCheckWorkload<StackWorkload>(*stackLayer, factory);
2355  StackQueueDescriptor queueDescriptor = stackWorkload->GetData();
2356  CHECK(queueDescriptor.m_Inputs.size() == numInputs);
2357  CHECK(queueDescriptor.m_Outputs.size() == 1);
2358 
2359  return stackWorkload;
2360 }
2361 
2362 } // Anonymous namespace
armnn::InputLayer
A layer user-provided data can be bound to (e.g. inputs, outputs).
Definition: InputLayer.hpp:13
ARMNN_ASSERT
#define ARMNN_ASSERT(COND)
Definition: Assert.hpp:14
armnn::BatchNormalizationDescriptor
A BatchNormalizationDescriptor for the BatchNormalizationLayer.
Definition: Descriptors.hpp:828
armnn::Convolution2dDescriptor::m_PadTop
uint32_t m_PadTop
Padding top value in the height dimension.
Definition: Descriptors.hpp:570
armnn::BatchNormalizationQueueDescriptor
Definition: WorkloadData.hpp:311
armnn::LstmQueueDescriptor::m_CellBias
const ConstTensorHandle * m_CellBias
Definition: WorkloadData.hpp:440
armnn::SpaceToDepthQueueDescriptor
Definition: WorkloadData.hpp:390
armnn::INetworkPtr
std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr
Definition: INetwork.hpp:339
armnn::IOptimizedNetworkPtr
std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr
Definition: INetwork.hpp:340
armnn::QuantizedLstmQueueDescriptor::m_CellBias
const ConstTensorHandle * m_CellBias
Definition: WorkloadData.hpp:645
armnn::FullyConnectedDescriptor::m_ConstantWeights
bool m_ConstantWeights
Enable/disable constant weights and biases.
Definition: Descriptors.hpp:530
armnn::ViewsDescriptor
A ViewsDescriptor for the SplitterLayer.
Definition: Descriptors.hpp:244
armnn::QLstmDescriptor::m_ForgetIntermediateScale
float m_ForgetIntermediateScale
Forget intermediate quantization scale.
Definition: Descriptors.hpp:1428
armnn::ActivationDescriptor
An ActivationDescriptor for the ActivationLayer.
Definition: Descriptors.hpp:36
armnn::QuantizedLstmQueueDescriptor::m_RecurrentToInputWeights
const ConstTensorHandle * m_RecurrentToInputWeights
Definition: WorkloadData.hpp:638
armnn::FullyConnectedDescriptor
A FullyConnectedDescriptor for the FullyConnectedLayer.
Definition: Descriptors.hpp:507
armnn::NormalizationQueueDescriptor
Definition: WorkloadData.hpp:252
armnn::QLstmDescriptor
A QLstmDescriptor for the QLstmLayer.
Definition: Descriptors.hpp:1380
armnn::FullyConnectedQueueDescriptor
Definition: WorkloadData.hpp:180
armnn::QuantizedLstmQueueDescriptor::m_InputToForgetWeights
const ConstTensorHandle * m_InputToForgetWeights
Definition: WorkloadData.hpp:634
armnn::QLstmBasicParameters::m_ForgetGateBias
std::shared_ptr< ConstTensorHandle > m_ForgetGateBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32).
Definition: QLstmLayer.hpp:31
armnn::SplitterLayer
This layer represents a split operation.
Definition: SplitterLayer.hpp:13
armnn::ConcatLayer
This layer represents a merge operation.
Definition: ConcatLayer.hpp:13
armnn::Compute::GpuAcc
@ GpuAcc
GPU Execution: OpenCL: ArmCompute.
armnn::QLstmDescriptor::m_ProjectionEnabled
bool m_ProjectionEnabled
Enable/disable the projection layer.
Definition: Descriptors.hpp:1422
armnn::BatchNormalizationDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:843
armnn::BatchNormalizationQueueDescriptor::m_Gamma
const ConstTensorHandle * m_Gamma
Definition: WorkloadData.hpp:324
armnn::DataLayout
DataLayout
Definition: Types.hpp:62
armnn::QLstmLayer
This layer represents a QLstm operation.
Definition: QLstmLayer.hpp:79
WorkloadData.hpp
armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix
bool m_TransposeWeightMatrix
Enable/disable transpose weight matrix.
Definition: Descriptors.hpp:528
armnn::ResizeDescriptor::m_TargetHeight
uint32_t m_TargetHeight
Target height value.
Definition: Descriptors.hpp:1009
armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled
bool m_BiasEnabled
Enable/disable bias.
Definition: Descriptors.hpp:708
armnn::Pooling2dDescriptor::m_PoolHeight
uint32_t m_PoolHeight
Pooling height value.
Definition: Descriptors.hpp:417
armnn::QueueDescriptor::GetAdditionalInformation
const T * GetAdditionalInformation() const
Definition: WorkloadData.hpp:47
armnn::NormalizationLayer
This layer represents a normalization operation.
Definition: NormalizationLayer.hpp:13
armnn::LstmBasicParameters::m_InputToCellWeights
std::shared_ptr< ConstTensorHandle > m_InputToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units].
Definition: LstmParameters.hpp:59
armnn::DataLayout::NHWC
@ NHWC
armnn::LogSoftmaxLayer
This layer represents a log softmax operation.
Definition: LogSoftmaxLayer.hpp:14
armnn::BatchNormalizationLayer::m_Mean
std::shared_ptr< ConstTensorHandle > m_Mean
A unique pointer to store Mean values.
Definition: BatchNormalizationLayer.hpp:19
armnn::ResizeDescriptor
A ResizeDescriptor for the ResizeLayer.
Definition: Descriptors.hpp:985
armnn::SubtractionLayer
This layer represents a subtraction operation.
Definition: SubtractionLayer.hpp:14
armnn::StackQueueDescriptor
Definition: WorkloadData.hpp:152
armnn::ActivationDescriptor::m_A
float m_A
Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH,...
Definition: Descriptors.hpp:61
armnn::QLstmQueueDescriptor::m_InputToForgetWeights
const ConstTensorHandle * m_InputToForgetWeights
Definition: WorkloadData.hpp:590
armnn::TensorHandleFactoryRegistry
Definition: TensorHandleFactoryRegistry.hpp:23
armnn::QLstmOptLayerNormParameters::m_ForgetLayerNormWeights
std::shared_ptr< ConstTensorHandle > m_ForgetLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:71
armnn::DepthwiseConvolution2dLayer
This layer represents a depthwise convolution 2d operation.
Definition: DepthwiseConvolution2dLayer.hpp:15
armnn::DepthwiseConvolution2dDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:710
armnn::BatchNormalizationQueueDescriptor::m_Variance
const ConstTensorHandle * m_Variance
Definition: WorkloadData.hpp:322
armnn::L2NormalizationDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:824
armnn::TensorInfo
Definition: Tensor.hpp:152
armnn::L2NormalizationDescriptor
A L2NormalizationDescriptor for the L2NormalizationLayer.
Definition: Descriptors.hpp:809
armnn::NormalizationAlgorithmMethod::LocalBrightness
@ LocalBrightness
Krichevsky 2012: Local Brightness Normalization.
armnn::NormalizationDescriptor::m_Beta
float m_Beta
Beta value for the normalization equation.
Definition: Descriptors.hpp:801
armnn::MeanLayer
This layer represents a mean operation.
Definition: MeanLayer.hpp:14
armnn::LstmQueueDescriptor::m_InputToForgetWeights
const ConstTensorHandle * m_InputToForgetWeights
Definition: WorkloadData.hpp:428
Graph.hpp
armnn::NormalizationDescriptor
A NormalizationDescriptor for the NormalizationLayer.
Definition: Descriptors.hpp:769
armnn::OutputShapeRounding::Floor
@ Floor
armnn::BatchToSpaceNdQueueDescriptor
Definition: WorkloadData.hpp:462
armnn::Pooling2dDescriptor::m_StrideY
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
Definition: Descriptors.hpp:421
armnnUtils::DataLayoutIndexed
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout.
Definition: DataLayoutIndexed.hpp:17
armnn::DataType::Float32
@ Float32
armnn::L2NormalizationQueueDescriptor
Definition: WorkloadData.hpp:358
armnn::ResizeDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:1014
armnn::PreluLayer
Definition: PreluLayer.hpp:14
armnn::QLstmOptLayerNormParameters::m_OutputLayerNormWeights
std::shared_ptr< ConstTensorHandle > m_OutputLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:75
armnn::DepthwiseConvolution2dDescriptor::m_PadLeft
uint32_t m_PadLeft
Padding left value in the width dimension.
Definition: Descriptors.hpp:692
armnn::ConvertFp32ToFp16QueueDescriptor
Definition: WorkloadData.hpp:457
armnn::Convolution2dDescriptor::m_StrideY
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
Definition: Descriptors.hpp:576
armnn::Pooling2dDescriptor::m_PadTop
uint32_t m_PadTop
Padding top value in the height dimension.
Definition: Descriptors.hpp:411
armnn::BatchNormalizationQueueDescriptor::m_Mean
const ConstTensorHandle * m_Mean
Definition: WorkloadData.hpp:321
armnn::QuantizedLstmQueueDescriptor::m_RecurrentToOutputWeights
const ConstTensorHandle * m_RecurrentToOutputWeights
Definition: WorkloadData.hpp:641
armnn::QuantizedLstmQueueDescriptor::m_InputGateBias
const ConstTensorHandle * m_InputGateBias
Definition: WorkloadData.hpp:643
armnn::DataType::QAsymmU8
@ QAsymmU8
armnn::QLstmDescriptor::m_InputIntermediateScale
float m_InputIntermediateScale
Input intermediate quantization scale.
Definition: Descriptors.hpp:1426
armnn::PreCompiledQueueDescriptor
Definition: WorkloadData.hpp:512
ResolveType.hpp
armnn::ActivationFunction::BoundedReLu
@ BoundedReLu
min(a, max(b, input)) ReLu1 & ReLu6.
armnn::ConstTensorHandle::GetTensorInfo
const TensorInfo & GetTensorInfo() const
Definition: TensorHandle.hpp:40
armnn::DataType::QSymmS8
@ QSymmS8
armnn::StackDescriptor
A StackDescriptor for the StackLayer.
Definition: Descriptors.hpp:1251
armnn::IWorkloadFactory::GetBackendId
virtual const BackendId & GetBackendId() const =0
IgnoreUnused.hpp
armnn::NormalizationDescriptor::m_NormSize
uint32_t m_NormSize
Depth radius value.
Definition: Descriptors.hpp:797
armnn::StackLayer
This layer represents a stack operation.
Definition: StackLayer.hpp:13
armnn::Pooling2dDescriptor::m_PoolWidth
uint32_t m_PoolWidth
Pooling width value.
Definition: Descriptors.hpp:415
armnn::BatchNormalizationLayer
This layer represents a batch normalization operation.
Definition: BatchNormalizationLayer.hpp:15
armnn::Convolution2dDescriptor::m_PadLeft
uint32_t m_PadLeft
Padding left value in the width dimension.
Definition: Descriptors.hpp:566
armnn::DepthwiseConvolution2dDescriptor::m_StrideY
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
Definition: Descriptors.hpp:702
armnn::SplitterQueueDescriptor::m_ViewOrigins
std::vector< ViewOrigin > m_ViewOrigins
Definition: WorkloadData.hpp:124
armnn::PreluQueueDescriptor
Definition: WorkloadData.hpp:539
armnn::BatchToSpaceNdLayer
This layer represents a BatchToSpaceNd operation.
Definition: BatchToSpaceNdLayer.hpp:13
armnn::QLstmDescriptor::m_CellIntermediateScale
float m_CellIntermediateScale
Cell intermediate quantization scale.
Definition: Descriptors.hpp:1430
armnn::DataType::QSymmS16
@ QSymmS16
armnn::LstmBasicParameters::m_InputToOutputWeights
std::shared_ptr< ConstTensorHandle > m_InputToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units].
Definition: LstmParameters.hpp:61
armnn::NormalizationDescriptor::m_NormMethodType
NormalizationAlgorithmMethod m_NormMethodType
Normalization method algorithm to use (LocalBrightness, LocalContrast).
Definition: Descriptors.hpp:795
WorkloadFactory.hpp
armnn::Layer::CreateTensorHandles
virtual void CreateTensorHandles(const TensorHandleFactoryRegistry &registry, const IWorkloadFactory &factory, const bool IsMemoryManaged=true)
Definition: Layer.cpp:292
armnn::NormalizationAlgorithmChannel::Across
@ Across
armnn::ActivationQueueDescriptor
Definition: WorkloadData.hpp:158
armnn::QLstmQueueDescriptor::m_RecurrentToOutputWeights
const ConstTensorHandle * m_RecurrentToOutputWeights
Definition: WorkloadData.hpp:596
armnnUtils::DataLayoutIndexed::GetHeightIndex
unsigned int GetHeightIndex() const
Definition: DataLayoutIndexed.hpp:24
armnn::Convolution2dLayer
This layer represents a convolution 2d operation.
Definition: Convolution2dLayer.hpp:15
armnn::Layer::CreateWorkload
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const =0
armnn::MeanDescriptor::m_KeepDims
bool m_KeepDims
Enable/disable keep dimensions. If true, then the reduced dimensions that are of length 1 are kept.
Definition: Descriptors.hpp:1192
armnn::QLstmOptLayerNormParameters::m_CellLayerNormWeights
std::shared_ptr< ConstTensorHandle > m_CellLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:73
armnn::Layer
Definition: Layer.hpp:230
armnn::ElementwiseBinaryDescriptor
A ElementwiseBinaryDescriptor for the ElementwiseBinaryLayer.
Definition: Descriptors.hpp:109
Assert.hpp
armnn::LstmBasicParameters::m_ForgetGateBias
std::shared_ptr< ConstTensorHandle > m_ForgetGateBias
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmParameters.hpp:69
armnn::AdditionLayer
This layer represents an addition operation.
Definition: AdditionLayer.hpp:13
armnn::BatchNormalizationLayer::m_Gamma
std::shared_ptr< ConstTensorHandle > m_Gamma
A unique pointer to store Gamma values.
Definition: BatchNormalizationLayer.hpp:25
armnn::SplitterQueueDescriptor
Definition: WorkloadData.hpp:111
armnn::ResizeDescriptor::m_Method
ResizeMethod m_Method
The Interpolation method to use (Bilinear, NearestNeighbor).
Definition: Descriptors.hpp:1012
armnn::LstmDescriptor::m_PeepholeEnabled
bool m_PeepholeEnabled
Enable/disable peephole.
Definition: Descriptors.hpp:1148
armnn::TensorShape
Definition: Tensor.hpp:20
armnn::NormalizationDescriptor::m_NormChannelType
NormalizationAlgorithmChannel m_NormChannelType
Normalization channel algorithm to use (Across, Within).
Definition: Descriptors.hpp:793
armnn::Layer::GetAdditionalInformation
std::shared_ptr< T > GetAdditionalInformation() const
Definition: Layer.hpp:368
armnn::QuantizedLstmQueueDescriptor::m_RecurrentToCellWeights
const ConstTensorHandle * m_RecurrentToCellWeights
Definition: WorkloadData.hpp:640
armnn::ReshapeLayer
This layer represents a reshape operation.
Definition: ReshapeLayer.hpp:15
armnn::DataType::Float16
@ Float16
armnn::LstmDescriptor::m_ClippingThresProj
float m_ClippingThresProj
Clipping threshold value for the projection.
Definition: Descriptors.hpp:1144
armnn::ConvertFp32ToFp16Layer
This layer converts data type Float 32 to Float 16.
Definition: ConvertFp32ToFp16Layer.hpp:13
armnn::Pooling2dDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:427
armnn::ConvertFp16ToFp32Layer
This layer converts data type Float 16 to Float 32.
Definition: ConvertFp16ToFp32Layer.hpp:14
armnn::LstmLayer
This layer represents a LSTM operation.
Definition: LstmLayer.hpp:16
armnn::QueueDescriptorWithParameters::m_Parameters
LayerDescriptor m_Parameters
Definition: WorkloadData.hpp:66
armnn::BatchNormalizationLayer::m_Variance
std::shared_ptr< ConstTensorHandle > m_Variance
A unique pointer to store Variance values.
Definition: BatchNormalizationLayer.hpp:21
armnn::Pooling2dDescriptor::m_PadBottom
uint32_t m_PadBottom
Padding bottom value in the height dimension.
Definition: Descriptors.hpp:413
armnn::Convolution2dQueueDescriptor
Definition: WorkloadData.hpp:210
armnn::Pooling2dDescriptor::m_PadRight
uint32_t m_PadRight
Padding right value in the width dimension.
Definition: Descriptors.hpp:409
armnn::FullyConnectedDescriptor::m_BiasEnabled
bool m_BiasEnabled
Enable/disable bias.
Definition: Descriptors.hpp:526
armnn::IRuntimePtr
std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr
Definition: IRuntime.hpp:41
armnn::QuantizedLstmQueueDescriptor::m_InputToInputWeights
const ConstTensorHandle * m_InputToInputWeights
Definition: WorkloadData.hpp:633
armnn::IOutputSlot::SetTensorInfo
virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0
armnn::MultiplicationLayer
This layer represents a multiplication operation.
Definition: MultiplicationLayer.hpp:14
PolymorphicDowncast.hpp
armnn::DataType
DataType
Definition: Types.hpp:48
armnn::SpaceToDepthLayer
This layer represents a SpaceToDepth operation.
Definition: SpaceToDepthLayer.hpp:14
armnn::Convolution2dDescriptor::m_BiasEnabled
bool m_BiasEnabled
Enable/disable bias.
Definition: Descriptors.hpp:582
armnn::ReshapeDescriptor
A ReshapeDescriptor for the ReshapeLayer.
Definition: Descriptors.hpp:1023
armnn::OutputLayer
A layer user-provided data can be bound to (e.g. inputs, outputs).
Definition: OutputLayer.hpp:13
armnn::IWorkloadFactory
Definition: WorkloadFactory.hpp:22
armnn::BatchNormalizationQueueDescriptor::m_Beta
const ConstTensorHandle * m_Beta
Definition: WorkloadData.hpp:323
armnn::QuantizedLstmQueueDescriptor::m_RecurrentToForgetWeights
const ConstTensorHandle * m_RecurrentToForgetWeights
Definition: WorkloadData.hpp:639
armnn::DepthwiseConvolution2dDescriptor::m_PadRight
uint32_t m_PadRight
Padding right value in the width dimension.
Definition: Descriptors.hpp:694
armnn::L2NormalizationLayer
This layer represents a L2 normalization operation.
Definition: L2NormalizationLayer.hpp:13
armnn::ActivationDescriptor::m_Function
ActivationFunction m_Function
The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu,...
Definition: Descriptors.hpp:59
armnn::QLstmQueueDescriptor::m_OutputGateBias
const ConstTensorHandle * m_OutputGateBias
Definition: WorkloadData.hpp:603
armnn::Layer::SetAdditionalInfoForObject
void SetAdditionalInfoForObject(const AdditionalInfoObjectPtr &additionalInfo)
Definition: Layer.hpp:373
TestUtils.hpp
armnn::NormalizationDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:805
armnn::QLstmQueueDescriptor::m_ForgetGateBias
const ConstTensorHandle * m_ForgetGateBias
Definition: WorkloadData.hpp:601
armnn::FullyConnectedLayer
This layer represents a fully connected operation.
Definition: FullyConnectedLayer.hpp:15
armnn::Convolution2dDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:584
armnn::QuantizedLstmQueueDescriptor::m_InputToCellWeights
const ConstTensorHandle * m_InputToCellWeights
Definition: WorkloadData.hpp:635
armnn::QLstmDescriptor::m_ProjectionClip
float m_ProjectionClip
Clipping threshold value for the projection.
Definition: Descriptors.hpp:1416
armnn::Convolution2dDescriptor::m_PadBottom
uint32_t m_PadBottom
Padding bottom value in the height dimension.
Definition: Descriptors.hpp:572
armnn::ResolveType
typename ResolveTypeImpl< DT >::Type ResolveType
Definition: ResolveType.hpp:79
armnn::QLstmQueueDescriptor
Definition: WorkloadData.hpp:562
armnn::ReshapeDescriptor::m_TargetShape
TensorShape m_TargetShape
Target shape value.
Definition: Descriptors.hpp:1039
armnnUtils::DataLayoutIndexed::GetWidthIndex
unsigned int GetWidthIndex() const
Definition: DataLayoutIndexed.hpp:25
armnn::QuantizedLstmLayer
This layer represents a QuantizedLstm operation.
Definition: QuantizedLstmLayer.hpp:45
armnn::ElementwiseBinaryLayer
This layer represents a elementwiseBinary operation.
Definition: ElementwiseBinaryLayer.hpp:14
armnn::Pooling2dLayer
This layer represents a pooling 2d operation.
Definition: Pooling2dLayer.hpp:13
armnn::Pooling2dDescriptor::m_PadLeft
uint32_t m_PadLeft
Padding left value in the width dimension.
Definition: Descriptors.hpp:407
armnn::ActivationFunction
ActivationFunction
Definition: Types.hpp:86
armnn::UnaryOperation
UnaryOperation
Definition: Types.hpp:125
armnn::Convolution2dDescriptor::m_StrideX
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
Definition: Descriptors.hpp:574
armnn::QLstmDescriptor::m_OutputIntermediateScale
float m_OutputIntermediateScale
Output intermediate quantization scale.
Definition: Descriptors.hpp:1432
armnn::Convolution2dDescriptor::m_PadRight
uint32_t m_PadRight
Padding right value in the width dimension.
Definition: Descriptors.hpp:568
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkloadData.hpp:27
armnn::PoolingAlgorithm::Average
@ Average
armnn::IWorkloadFactory::IsLayerSupported
static bool IsLayerSupported(const BackendId &backendId, const IConnectableLayer &layer, Optional< DataType > dataType, std::string &outReasonIfUnsupported)
Definition: WorkloadFactory.cpp:1614
armnn::QuantizedLstmQueueDescriptor::m_InputToOutputWeights
const ConstTensorHandle * m_InputToOutputWeights
Definition: WorkloadData.hpp:636
armnn::DataType::Signed32
@ Signed32
armnn::LstmBasicParameters::m_OutputGateBias
std::shared_ptr< ConstTensorHandle > m_OutputGateBias
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmParameters.hpp:73
armnn::LstmBasicParameters::m_CellBias
std::shared_ptr< ConstTensorHandle > m_CellBias
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmParameters.hpp:71
armnn::BatchToSpaceNdDescriptor
A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer.
Definition: Descriptors.hpp:875
armnn::QLstmQueueDescriptor::m_RecurrentToForgetWeights
const ConstTensorHandle * m_RecurrentToForgetWeights
Definition: WorkloadData.hpp:594
armnn::Convolution2dDescriptor
A Convolution2dDescriptor for the Convolution2dLayer.
Definition: Descriptors.hpp:534
armnn::DepthwiseConvolution2dDescriptor::m_PadBottom
uint32_t m_PadBottom
Padding bottom value in the height dimension.
Definition: Descriptors.hpp:698
armnn::DataType::QAsymmS8
@ QAsymmS8
armnn::DepthwiseConvolution2dQueueDescriptor
Depthwise Convolution 2D layer workload data.
Definition: WorkloadData.hpp:234
armnn::QuantizedLstmQueueDescriptor
Definition: WorkloadData.hpp:614
armnn::ResizeMethod::Bilinear
@ Bilinear
armnn::QLstmLayer::m_LayerNormParameters
QLstmOptLayerNormParameters m_LayerNormParameters
Definition: QLstmLayer.hpp:87
armnn::ReshapeQueueDescriptor
Definition: WorkloadData.hpp:380
armnn::Pooling2dDescriptor::m_StrideX
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
Definition: Descriptors.hpp:419
armnn::SpaceToDepthDescriptor::m_BlockSize
unsigned int m_BlockSize
Scalar specifying the input block size. It must be >= 1.
Definition: Descriptors.hpp:1092
armnn::CreateDescriptorForConcatenation
OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)
Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...
Definition: Descriptors.hpp:300
armnn::TensorInfo::SetQuantizationOffset
void SetQuantizationOffset(int32_t offset)
Definition: Tensor.cpp:489
armnn::BatchNormalizationLayer::m_Beta
std::shared_ptr< ConstTensorHandle > m_Beta
A unique pointer to store Beta values.
Definition: BatchNormalizationLayer.hpp:23
armnn::QLstmBasicParameters::m_RecurrentToOutputWeights
std::shared_ptr< ConstTensorHandle > m_RecurrentToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8).
Definition: QLstmLayer.hpp:28
armnn::ResizeDescriptor::m_TargetWidth
uint32_t m_TargetWidth
Target width value.
Definition: Descriptors.hpp:1007
armnn::LogSoftmaxQueueDescriptor
Definition: WorkloadData.hpp:363
armnn::LstmLayer::m_BasicParameters
LstmBasicParameters m_BasicParameters
Definition: LstmLayer.hpp:20
armnn::Layer::GetDataType
DataType GetDataType() const
Definition: Layer.cpp:326
armnn::Pooling2dQueueDescriptor
Definition: WorkloadData.hpp:197
armnn::SoftmaxQueueDescriptor
Definition: WorkloadData.hpp:105
armnn::LstmBasicParameters::m_InputToForgetWeights
std::shared_ptr< ConstTensorHandle > m_InputToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units].
Definition: LstmParameters.hpp:57
armnn::LstmDescriptor
An LstmDescriptor for the LstmLayer.
Definition: Descriptors.hpp:1102
armnn::LstmQueueDescriptor
Definition: WorkloadData.hpp:400
TensorHandle.hpp
armnn::MeanDescriptor::m_Axis
std::vector< unsigned int > m_Axis
Values for the dimensions to reduce.
Definition: Descriptors.hpp:1190
armnn::LstmDescriptor::m_CifgEnabled
bool m_CifgEnabled
Enable/disable cifg (coupled input & forget gate).
Definition: Descriptors.hpp:1146
armnn::IOutputSlot::Connect
virtual int Connect(IInputSlot &destination)=0
armnn::IRuntime::CreationOptions
Definition: IRuntime.hpp:78
armnn::NormalizationDescriptor::m_Alpha
float m_Alpha
Alpha value for the normalization equation.
Definition: Descriptors.hpp:799
armnn::BinaryOperation
BinaryOperation
Definition: Types.hpp:138
armnn::QLstmQueueDescriptor::m_InputToOutputWeights
const ConstTensorHandle * m_InputToOutputWeights
Definition: WorkloadData.hpp:592
armnn::LstmBasicParameters::m_RecurrentToCellWeights
std::shared_ptr< ConstTensorHandle > m_RecurrentToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units].
Definition: LstmParameters.hpp:65
armnn::ActivationLayer
This layer represents an activation operation with the specified activation function.
Definition: ActivationLayer.hpp:12
armnn::LstmBasicParameters::m_RecurrentToForgetWeights
std::shared_ptr< ConstTensorHandle > m_RecurrentToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units].
Definition: LstmParameters.hpp:63
armnn::SoftmaxLayer
This layer represents a softmax operation.
Definition: SoftmaxLayer.hpp:13
Network.hpp
armnn::QLstmDescriptor::m_HiddenStateZeroPoint
int32_t m_HiddenStateZeroPoint
Hidden State zero point.
Definition: Descriptors.hpp:1434
armnn::SoftmaxDescriptor::m_Axis
int m_Axis
Scalar, defaulted to the last index (-1), specifying the dimension the activation will be performed o...
Definition: Descriptors.hpp:192
armnn::LstmBasicParameters::m_RecurrentToOutputWeights
std::shared_ptr< ConstTensorHandle > m_RecurrentToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units].
Definition: LstmParameters.hpp:67
armnn::IRuntime::Create
static IRuntimePtr Create(const CreationOptions &options)
Definition: Runtime.cpp:52
armnn::GetBiasDataType
DataType GetBiasDataType(DataType inputDataType)
Definition: WorkloadData.cpp:28
armnn::ConstantLayer::m_LayerOutput
std::shared_ptr< ConstTensorHandle > m_LayerOutput
Definition: ConstantLayer.hpp:46
std
Definition: BackendId.hpp:149
armnn::QLstmBasicParameters::m_CellBias
std::shared_ptr< ConstTensorHandle > m_CellBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32).
Definition: QLstmLayer.hpp:33
armnn::Graph::TopologicalSort
Graph & TopologicalSort()
Sorts layers in topological order and return this.
Definition: Graph.hpp:184
armnn::QLstmBasicParameters::m_OutputGateBias
std::shared_ptr< ConstTensorHandle > m_OutputGateBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32).
Definition: QLstmLayer.hpp:35
armnn::IgnoreUnused
void IgnoreUnused(Ts &&...)
Definition: IgnoreUnused.hpp:14
armnn::QLstmBasicParameters::m_RecurrentToCellWeights
std::shared_ptr< ConstTensorHandle > m_RecurrentToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8).
Definition: QLstmLayer.hpp:26
armnn::LayerType::PreCompiled
@ PreCompiled
armnn::QLstmDescriptor::m_CifgEnabled
bool m_CifgEnabled
Enable/disable CIFG (coupled input & forget gate).
Definition: Descriptors.hpp:1418
armnn::Graph::AddLayer
LayerT * AddLayer(Args &&... args)
Adds a new layer, of type LayerType, to the graph constructed with the arguments passed.
Definition: Graph.hpp:456
armnn::OriginsDescriptor
An OriginsDescriptor for the ConcatLayer.
Definition: Descriptors.hpp:201
armnn::Compute::CpuAcc
@ CpuAcc
CPU Execution: NEON: ArmCompute.
armnn::ConvertFp16ToFp32QueueDescriptor
Definition: WorkloadData.hpp:452
armnn::LstmOptPeepholeParameters::m_CellToOutputWeights
std::shared_ptr< ConstTensorHandle > m_CellToOutputWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmParameters.hpp:51
armnn::ActivationFunction::ReLu
@ ReLu
armnn::ConstantLayer
A layer that the constant data can be bound to.
Definition: ConstantLayer.hpp:15
armnn::IConnectableLayer::GetOutputSlot
virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0
Get the const output slot handle by slot index.
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
armnn::ElementwiseUnaryDescriptor
A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer.
Definition: Descriptors.hpp:129
armnn::IConnectableLayer::GetInputSlot
virtual const IInputSlot & GetInputSlot(unsigned int index) const =0
Get a const input slot handle by slot index.
armnn::ActivationDescriptor::m_B
float m_B
Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH).
Definition: Descriptors.hpp:63
armnn::Layer::SetBackendId
void SetBackendId(const BackendId &id) override
Set the backend of the IConnectableLayer.
Definition: Layer.hpp:291
armnn::ElementwiseUnaryLayer
This layer represents a elementwiseUnary operation.
Definition: ElementwiseUnaryLayer.hpp:14
armnn::GetGraphForTesting
Graph & GetGraphForTesting(IOptimizedNetwork *optNet)
Definition: TestUtils.cpp:49
armnn::QLstmDescriptor::m_HiddenStateScale
float m_HiddenStateScale
Hidden State quantization scale.
Definition: Descriptors.hpp:1436
armnn::NormalizationDescriptor::m_K
float m_K
Kappa value used for the across channel normalization equation.
Definition: Descriptors.hpp:803
armnn::LstmDescriptor::m_ProjectionEnabled
bool m_ProjectionEnabled
Enable/disable the projection layer.
Definition: Descriptors.hpp:1150
armnn::ConstTensor
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
Definition: Tensor.hpp:329
armnn::IConnectableLayer
Interface for a layer that is connectable to other layers via InputSlots and OutputSlots.
Definition: INetwork.hpp:80
armnn::QLstmBasicParameters::m_RecurrentToForgetWeights
std::shared_ptr< ConstTensorHandle > m_RecurrentToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8).
Definition: QLstmLayer.hpp:24
armnn::Pooling2dDescriptor::m_OutputShapeRounding
OutputShapeRounding m_OutputShapeRounding
The rounding method for the output shape. (Floor, Ceiling).
Definition: Descriptors.hpp:423
armnn::LstmQueueDescriptor::m_OutputGateBias
const ConstTensorHandle * m_OutputGateBias
Definition: WorkloadData.hpp:441
armnn::ModelOptions
std::vector< BackendOptions > ModelOptions
Definition: BackendOptions.hpp:18
armnn::QLstmDescriptor::m_CellClip
float m_CellClip
Clipping threshold value for the cell state.
Definition: Descriptors.hpp:1414
armnn::QLstmDescriptor::m_LayerNormEnabled
bool m_LayerNormEnabled
Enable/disable layer normalization.
Definition: Descriptors.hpp:1424
armnn::TensorInfo::SetConstant
void SetConstant(const bool IsConstant=true)
Marks the data corresponding to this tensor info as constant.
Definition: Tensor.cpp:514
armnn::ResizeLayer
This layer represents a resize operation.
Definition: ResizeLayer.hpp:13
armnn::Pooling2dDescriptor
A Pooling2dDescriptor for the Pooling2dLayer.
Definition: Descriptors.hpp:371
armnn::LstmDescriptor::m_ActivationFunc
uint32_t m_ActivationFunc
The activation function to use.
Definition: Descriptors.hpp:1140
armnn::Optimize
IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptionsOpaque &options=OptimizerOptionsOpaque(), Optional< std::vector< std::string > & > messages=EmptyOptional())
Create an optimized version of the network.
Definition: Network.cpp:2132
armnn::QLstmQueueDescriptor::m_CellBias
const ConstTensorHandle * m_CellBias
Definition: WorkloadData.hpp:602
armnn::DepthwiseConvolution2dDescriptor
A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer.
Definition: Descriptors.hpp:659
armnn::LstmLayer::m_PeepholeParameters
LstmOptPeepholeParameters m_PeepholeParameters
Definition: LstmLayer.hpp:23
armnn::MeanQueueDescriptor
Definition: WorkloadData.hpp:288
armnn::QLstmLayer::m_BasicParameters
QLstmBasicParameters m_BasicParameters
Definition: QLstmLayer.hpp:83
armnn::QLstmQueueDescriptor::m_InputToCellWeights
const ConstTensorHandle * m_InputToCellWeights
Definition: WorkloadData.hpp:591
armnn::BatchNormalizationDescriptor::m_Eps
float m_Eps
Value to add to the variance. Used to avoid dividing by zero.
Definition: Descriptors.hpp:841
armnn::LstmDescriptor::m_ClippingThresCell
float m_ClippingThresCell
Clipping threshold value for the cell state.
Definition: Descriptors.hpp:1142
armnn::INetwork::Create
static INetworkPtr Create(const NetworkOptions &networkOptions={})
Definition: Network.cpp:676
armnn::LayerType
LayerType
When adding a new layer, adapt also the LastLayer enum value in the enum class LayerType below.
Definition: Types.hpp:491
DataLayoutIndexed.hpp
armnn::QLstmBasicParameters::m_InputToOutputWeights
std::shared_ptr< ConstTensorHandle > m_InputToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8).
Definition: QLstmLayer.hpp:21
armnn::MeanDescriptor
A MeanDescriptor for the MeanLayer.
Definition: Descriptors.hpp:1172
armnn::QLstmDescriptor::m_PeepholeEnabled
bool m_PeepholeEnabled
Enable/disable peephole.
Definition: Descriptors.hpp:1420
armnn::QuantizedLstmQueueDescriptor::m_OutputGateBias
const ConstTensorHandle * m_OutputGateBias
Definition: WorkloadData.hpp:646
armnn::Graph
Definition: Graph.hpp:30
armnn::SoftmaxDescriptor
A SoftmaxDescriptor for the SoftmaxLayer.
Definition: Descriptors.hpp:177
armnn::Pooling2dDescriptor::m_PoolType
PoolingAlgorithm m_PoolType
The pooling algorithm to use (Max. Average, L2).
Definition: Descriptors.hpp:405
armnn::QLstmBasicParameters::m_InputToForgetWeights
std::shared_ptr< ConstTensorHandle > m_InputToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8).
Definition: QLstmLayer.hpp:17
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkloadData.hpp:26
armnn::SpaceToDepthDescriptor
A SpaceToDepthDescriptor for the SpaceToDepthLayer.
Definition: Descriptors.hpp:1075
armnn::QLstmQueueDescriptor::m_RecurrentToCellWeights
const ConstTensorHandle * m_RecurrentToCellWeights
Definition: WorkloadData.hpp:595
Connect
void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)
Definition: TestUtils.cpp:14
armnn::DataLayout::NCHW
@ NCHW
armnn::LstmOptPeepholeParameters::m_CellToForgetWeights
std::shared_ptr< ConstTensorHandle > m_CellToForgetWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units].
Definition: LstmParameters.hpp:49
armnn::QuantizedLstmQueueDescriptor::m_ForgetGateBias
const ConstTensorHandle * m_ForgetGateBias
Definition: WorkloadData.hpp:644
armnn::QLstmBasicParameters::m_InputToCellWeights
std::shared_ptr< ConstTensorHandle > m_InputToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8).
Definition: QLstmLayer.hpp:19
armnn::DepthwiseConvolution2dDescriptor::m_StrideX
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
Definition: Descriptors.hpp:700
armnn::DepthwiseConvolution2dDescriptor::m_PadTop
uint32_t m_PadTop
Padding top value in the height dimension.
Definition: Descriptors.hpp:696
armnn::OptimizerOptionsOpaque
Definition: INetwork.hpp:272