16 #include <boost/test/unit_test.hpp> 18 using namespace armnn;
37 unsigned int inputShape[] = {2, 3, 4};
38 unsigned int outputShape[] = {2, 3, 4, 5};
46 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
47 AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo,
nullptr);
55 unsigned int inputHeight = 1;
56 unsigned int inputWidth = 1;
57 unsigned int inputChannels = 4;
58 unsigned int inputNum = 2;
60 unsigned int outputChannels = inputChannels;
61 unsigned int outputHeight = inputHeight + 1;
62 unsigned int outputWidth = inputWidth;
63 unsigned int outputNum = inputNum;
68 unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
69 unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };
77 AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo,
nullptr);
78 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
86 unsigned int inputWidth = 1;
87 unsigned int inputHeight = 1;
88 unsigned int inputChannels = 5;
89 unsigned int inputNum = 2;
91 unsigned int outputWidth = 1;
92 unsigned int outputHeight = 1;
93 unsigned int outputChannels = 3;
94 unsigned int outputNum = 2;
102 unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
103 unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };
104 unsigned int weightsShape[] = { 1, 1, inputChannels, outputChannels };
105 unsigned int biasShape[] = { 1, outputChannels, outputHeight, outputWidth };
118 AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo,
nullptr);
119 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
120 invalidData.
m_Weight = &weightTensor;
121 invalidData.
m_Bias = &biasTensor;
134 constexpr
unsigned int inputNum = 5;
135 constexpr
unsigned int inputHeight = 32;
136 constexpr
unsigned int inputWidth = 24;
137 constexpr
unsigned int inputChannels = 3;
139 constexpr
unsigned int outputNum = inputNum;
140 constexpr
unsigned int outputChannels = inputChannels;
141 constexpr
unsigned int outputHeight = inputHeight + 1;
143 constexpr
unsigned int outputWidth = inputWidth;
149 unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};
150 unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
161 uint32_t normSize = 5;
166 AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo,
nullptr);
167 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
181 constexpr
unsigned int inputNum = 1;
182 constexpr
unsigned int inputHeight = 32;
183 constexpr
unsigned int inputWidth = 24;
184 constexpr
unsigned int inputChannels = 3;
186 constexpr
unsigned int outputNum = inputNum;
187 constexpr
unsigned int outputChannels = inputChannels;
188 constexpr
unsigned int outputHeight = 18;
189 constexpr
unsigned int outputWidth = inputWidth;
195 unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};
196 unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
204 AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo,
nullptr);
205 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
208 std::vector<unsigned int> wOrigin = {0, 0, 0};
212 BOOST_TEST_INFO(
"Invalid argument exception is expected, because split window dimensionality does not " 217 std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0};
220 BOOST_TEST_INFO(
"Invalid argument exception is expected (wOrigin3[2]+ outputHeight > inputHeight");
224 std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0};
228 std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2};
232 BOOST_TEST_INFO(
"Invalid exception due to number of split windows not matching number of outputs.");
239 constexpr
unsigned int inputNum = 1;
240 constexpr
unsigned int inputChannels = 3;
241 constexpr
unsigned int inputHeight = 32;
242 constexpr
unsigned int inputWidth = 24;
244 constexpr
unsigned int outputNum = 1;
245 constexpr
unsigned int outputChannels = 3;
246 constexpr
unsigned int outputHeight = 32;
247 constexpr
unsigned int outputWidth = 24;
253 unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};
254 unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
262 AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo,
nullptr);
263 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
266 std::vector<unsigned int> wOrigin = {0, 0, 0};
270 BOOST_TEST_INFO(
"Invalid argument exception is expected, because merge window dimensionality does not " 275 std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0};
278 BOOST_TEST_INFO(
"Invalid argument exception is expected (wOrigin3[2]+ inputHeight > outputHeight");
282 std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0};
286 std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2};
290 BOOST_TEST_INFO(
"Invalid exception due to number of merge windows not matching number of inputs.");
301 unsigned int shape[] = {1, 1, 1, 1};
311 AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo,
nullptr);
312 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
317 AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo,
nullptr);
322 AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo,
nullptr);
334 unsigned int shape1[] = {1, 1, 2, 1};
335 unsigned int shape2[] = {1, 1, 3, 2};
346 AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo,
nullptr);
347 AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo,
nullptr);
348 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
362 AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo,
nullptr);
363 AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo,
nullptr);
364 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
377 constexpr
unsigned int input0Shape[] = { 2, 2, 4, 4 };
378 constexpr std::size_t dimensionCount = std::extent<decltype(input0Shape)>::value;
381 for (
unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex)
383 unsigned int input1Shape[dimensionCount];
384 for (
unsigned int i = 0; i < dimensionCount; ++i)
386 input1Shape[i] = input0Shape[i];
389 ++input1Shape[dimIndex];
398 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
399 AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo,
nullptr);
400 AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo,
nullptr);
406 for (
unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex)
408 unsigned int outputShape[dimensionCount];
409 for (
unsigned int i = 0; i < dimensionCount; ++i)
411 outputShape[i] = input0Shape[i];
414 ++outputShape[dimIndex];
423 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
424 AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo,
nullptr);
425 AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo,
nullptr);
437 unsigned int inputShape[] = { 1, 1, 2, 3 };
438 unsigned int outputShape[] = { 1, 1, 1, 2 };
446 AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo,
nullptr);
447 AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo,
nullptr);
461 unsigned int batchSize = 2;
462 unsigned int outputSize = 3;
463 unsigned int inputSize = 5;
464 unsigned numUnits = 4;
466 armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, dataType, qScale, qOffset );
467 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, dataType, qScale, qOffset);
468 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, dataType, qScale, qOffset);
471 armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset);
472 armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, dataType, qScale, qOffset);
473 armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset);
474 armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset);
478 armnn::TensorInfo tensorInfo4x5({numUnits, inputSize}, dataType, qScale, qOffset);
479 armnn::TensorInfo tensorInfo4x3({numUnits, outputSize}, dataType, qScale, qOffset);
480 armnn::TensorInfo tensorInfo3x4({outputSize, numUnits}, dataType, qScale, qOffset);
485 AddInputToWorkload(data, info, inputTensorInfo,
nullptr);
486 AddInputToWorkload(data, info, outputStateInTensorInfo,
nullptr);
487 AddInputToWorkload(data, info, cellStateInTensorInfo,
nullptr);
489 AddOutputToWorkload(data, info, scratchBufferTensorInfo,
nullptr);
490 AddOutputToWorkload(data, info, outputStateOutTensorInfo,
nullptr);
491 AddOutputToWorkload(data, info, cellStateOutTensorInfo,
nullptr);
516 data.m_InputToInputWeights = &inputToInputWeightsTensor;
517 data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
518 data.m_InputToCellWeights = &inputToCellWeightsTensor;
519 data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
520 data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
521 data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
522 data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
523 data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
524 data.m_CellToInputWeights = &cellToInputWeightsTensor;
525 data.m_InputGateBias = &inputGateBiasTensor;
526 data.m_ForgetGateBias = &forgetGateBiasTensor;
527 data.m_CellBias = &cellBiasTensor;
528 data.m_OutputGateBias = &outputGateBiasTensor;
529 data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
530 data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
531 data.m_ProjectionWeights = &projectionWeightsTensor;
532 data.m_ProjectionBias = &projectionBiasTensor;
534 data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;
535 data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor;
536 data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor;
537 data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor;
540 data.m_Parameters.m_ActivationFunc = 4;
541 data.m_Parameters.m_CifgEnabled =
false;
542 data.m_Parameters.m_PeepholeEnabled =
true;
543 data.m_Parameters.m_ProjectionEnabled =
true;
544 data.m_Parameters.m_LayerNormEnabled =
true;
548 AddOutputToWorkload(data, info, outputTensorInfo,
nullptr);
551 data.m_Parameters.m_CifgEnabled =
true;
552 armnn::TensorInfo scratchBufferTensorInfo2({batchSize, numUnits * 3}, dataType, qScale, qOffset);
553 SetWorkloadOutput(data, info, 0, scratchBufferTensorInfo2,
nullptr);
555 data.m_Parameters.m_CifgEnabled =
false;
556 SetWorkloadOutput(data, info, 0, scratchBufferTensorInfo,
nullptr);
559 data.m_InputGateBias =
nullptr;
561 data.m_InputGateBias = &inputGateBiasTensor;
564 data.m_Parameters.m_ProjectionEnabled =
false;
566 data.m_Parameters.m_ProjectionEnabled =
true;
567 data.m_ProjectionWeights =
nullptr;
569 data.m_ProjectionWeights = &projectionWeightsTensor;
572 data.m_InputLayerNormWeights =
nullptr;
574 data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;
577 data.m_Parameters.m_LayerNormEnabled =
false;
579 data.m_Parameters.m_LayerNormEnabled =
true;
582 armnn::TensorInfo incorrectOutputTensorInfo({batchSize, outputSize + 1}, dataType, qScale, qOffset);
583 SetWorkloadOutput(data, info, 3, incorrectOutputTensorInfo,
nullptr);
585 SetWorkloadOutput(data, info, 3, outputTensorInfo,
nullptr);
588 data.m_Parameters.m_ClippingThresCell = -1.0f;
590 data.m_Parameters.m_ClippingThresCell = 0.0f;
593 data.m_Parameters.m_ClippingThresProj = -1.0f;
595 data.m_Parameters.m_ClippingThresProj = 0.0f;
598 BOOST_CHECK_NO_THROW(data.Validate(info));
603 constexpr
unsigned int nInput = 1u;
604 constexpr
unsigned int cInput = 3u;
605 constexpr
unsigned int hInput = 3u;
606 constexpr
unsigned int wInput = 3u;
608 constexpr
unsigned int nOutput = nInput;
609 constexpr
unsigned int cOutput = cInput;
610 constexpr
unsigned int hOutput = 1u;
611 constexpr
unsigned int wOutput = 1u;
613 const TensorShape inputShape { nInput, cInput, hInput, wInput };
614 const TensorShape outputShape{ nOutput, cOutput, hOutput, wOutput };
615 const TensorShape weightShape{ cOutput, cInput, hInput, wInput };
622 constexpr
float perTensorScale = 1.5f;
623 const TensorInfo inputInfo (inputShape, inputType, perTensorScale);
624 const TensorInfo outputInfo(outputShape, inputType, perTensorScale);
626 const std::vector<float> weightPerAxisScales = { 2.50f, 3.50f };
627 const TensorInfo weightInfo(weightShape, weightType, weightPerAxisScales, 0);
633 AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo,
nullptr);
634 AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo,
nullptr);
637 queueDescriptor.
m_Weight = &weightTensor;
640 const std::vector<float> biasPerAxisScales1 = { 3.75f, 5.25f };
641 const TensorInfo biasInfo1(biasShape, biasType, biasPerAxisScales1, 0);
644 queueDescriptor.
m_Bias = &biasHandle1;
646 BOOST_CHECK_NO_THROW(queueDescriptor.
Validate(workloadInfo));
649 const std::vector<float> biasPerAxisScales2 = { 4.00f, 5.00f };
650 const TensorInfo biasInfo2(biasShape, biasType, biasPerAxisScales2, 0);
653 queueDescriptor.
m_Bias = &biasHandle2;
658 const std::vector<float> biasPerAxisScales3 = { 3.75f, 5.25f, 5.25f };
659 const TensorInfo biasInfo3(biasShape, biasType, biasPerAxisScales3, 0);
662 queueDescriptor.
m_Bias = &biasHandle3;
float m_Alpha
Alpha value for the normalization equation.
const ConstCpuTensorHandle * m_Weight
LayerDescriptor m_Parameters
RefElementwiseWorkload< std::plus< float >, AdditionQueueDescriptor, StringMapping::RefAdditionWorkload_Execute > RefAdditionWorkload
BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)
void Validate(const WorkloadInfo &workloadInfo) const
const ConstCpuTensorHandle * m_Bias
bool m_BiasEnabled
Enable/disable bias.
const ConstCpuTensorHandle * m_Weight
Krichevsky 2012: Local Brightness Normalization.
std::vector< ViewOrigin > m_ViewOrigins
RefElementwiseWorkload< std::multiplies< float >, MultiplicationQueueDescriptor, StringMapping::RefMultiplicationWorkload_Execute > RefMultiplicationWorkload
uint32_t m_NormSize
Depth radius value.
float m_Beta
Beta value for the normalization equation.
bool m_TransposeWeightMatrix
Enable/disable transpose weight matrix.
NormalizationAlgorithmMethod
const ConstCpuTensorHandle * m_Bias
bool m_BiasEnabled
Enable/disable bias.
BOOST_AUTO_TEST_SUITE_END()
NormalizationAlgorithmMethod m_NormMethodType
Normalization method algorithm to use (LocalBrightness, LocalContrast).
float m_K
Kappa value used for the across channel normalization equation.
NormalizationAlgorithmChannel
std::vector< ViewOrigin > m_ViewOrigins
BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)
NormalizationAlgorithmChannel m_NormChannelType
Normalization channel algorithm to use (Across, Within).