#include "ConvertFp16ToFp32TestImpl.hpp"
#include "ConvertFp32ToFp16TestImpl.hpp"
+#include "ClContextControlFixture.hpp"
+
// 3-channel 16x8 image used as common input data for a number of Conv2d tests.
static std::vector<float> ConvInput3x8x16({
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
return ret;
}
-LayerTestResult<float, 4> L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory)
+namespace
{
- constexpr unsigned int inputWidth = 1;
- constexpr unsigned int inputHeight = 1;
- constexpr unsigned int inputChannels = 10;
- constexpr unsigned int inputBatchSize = 1;
-
- constexpr unsigned int outputWidth = inputWidth;
- constexpr unsigned int outputHeight = inputHeight;
- constexpr unsigned int outputChannels = inputChannels;
- constexpr unsigned int outputBatchSize = inputBatchSize;
- const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
- armnn::DataType::Float32);
- const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
- armnn::DataType::Float32);
+LayerTestResult<float, 4> L2NormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::TensorShape& inputOutputTensorShape,
+ const std::vector<float>& inputValues,
+ const std::vector<float>& expectedOutputValues,
+ armnn::DataLayout dataLayout)
+{
+ const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32);
+ const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32);
- auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
- 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f
- }));
+ auto inputTensor = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(inputValues));
- const float approxInvL2Norm = 0.050964719f;
LayerTestResult<float, 4> result(outputTensorInfo);
- result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
- 1.0f * approxInvL2Norm,
- 2.0f * approxInvL2Norm,
- 3.0f * approxInvL2Norm,
- 4.0f * approxInvL2Norm,
- 5.0f * approxInvL2Norm,
- 6.0f * approxInvL2Norm,
- 7.0f * approxInvL2Norm,
- 8.0f * approxInvL2Norm,
- 9.0f * approxInvL2Norm,
- 10.0f * approxInvL2Norm
- }));
+ result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(expectedOutputValues));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::L2NormalizationQueueDescriptor descriptor;
+ descriptor.m_Parameters.m_DataLayout = dataLayout;
armnn::WorkloadInfo info;
+
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
inputHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
return result;
}
-namespace
-{
-
float CalcInvL2Norm(std::initializer_list<float> elements)
{
const float reduction = std::accumulate(elements.begin(), elements.end(), 0.0f,
return 1.0f / sqrtf(reduction);
}
-}
+} // anonymous namespace
-LayerTestResult<float, 4> L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory)
+LayerTestResult<float, 4> L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory)
{
- constexpr unsigned int inputWidth = 5;
- constexpr unsigned int inputHeight = 1;
- constexpr unsigned int inputChannels = 2;
- constexpr unsigned int inputBatchSize = 1;
+ // Width: 1
+ // Height: 1
+ // Channels: 10
+ // BatchSize: 1
- constexpr unsigned int outputWidth = inputWidth;
- constexpr unsigned int outputHeight = inputHeight;
- constexpr unsigned int outputChannels = inputChannels;
- constexpr unsigned int outputBatchSize = inputBatchSize;
+ const armnn::TensorShape inputOutputShape{ 1, 10, 1, 1 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (1) x Width (1)
+ 1.0f,
- const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
- armnn::DataType::Float32);
- const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
- armnn::DataType::Float32);
+ // Batch 0, Channel 1, Height (1) x Width (1)
+ 2.0f,
- auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
- 1.0f, 3.0f, 5.0f, 7.0f, 9.0f,
- 2.0f, 4.0f, 6.0f, 8.0f, 10.0f
- }));
+ // Batch 0, Channel 2, Height (1) x Width (1)
+ 3.0f,
- LayerTestResult<float, 4> result(outputTensorInfo);
- result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
- 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
- 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
- 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
- 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
- 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),
+ // Batch 0, Channel 3, Height (1) x Width (1)
+ 4.0f,
- 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
- 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
- 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
- 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
- 10.0f * CalcInvL2Norm({ 9.0f, 10.0f })
- }));
+ // Batch 0, Channel 4, Height (1) x Width (1)
+ 5.0f,
- std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
- std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+ // Batch 0, Channel 5, Height (1) x Width (1)
+ 6.0f,
- armnn::L2NormalizationQueueDescriptor descriptor;
- armnn::WorkloadInfo info;
- AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
- AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+ // Batch 0, Channel 6, Height (1) x Width (1)
+ 7.0f,
- std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
+ // Batch 0, Channel 7, Height (1) x Width (1)
+ 8.0f,
- inputHandle->Allocate();
- outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+ // Batch 0, Channel 8, Height (1) x Width (1)
+ 9.0f,
- workloadFactory.Finalize();
- workload->Execute();
+ // Batch 0, Channel 9, Height (1) x Width (1)
+ 10.0f
+ };
+ const float approxInvL2Norm = 0.050964719f;
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (1) x Width (1)
+ 1.0f * approxInvL2Norm,
+ 2.0f * approxInvL2Norm,
+ 3.0f * approxInvL2Norm,
+ 4.0f * approxInvL2Norm,
+ 5.0f * approxInvL2Norm,
+ 6.0f * approxInvL2Norm,
+ 7.0f * approxInvL2Norm,
+ 8.0f * approxInvL2Norm,
+ 9.0f * approxInvL2Norm,
+ 10.0f * approxInvL2Norm
+ };
- CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
- return result;
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
}
-LayerTestResult<float, 4> L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory)
+LayerTestResult<float, 4> L2Normalization1dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
{
- constexpr unsigned int inputWidth = 3;
- constexpr unsigned int inputHeight = 4;
- constexpr unsigned int inputChannels = 2;
- constexpr unsigned int inputBatchSize = 1;
+#ifdef ARMCOMPUTECL_ENABLED
+ // Clear the CL cache before this test when using ACL
+ if (ClContextControlFixture::Instance())
+ {
+ ClContextControlFixture::Instance()->m_ClContextControl.ClearClCache();
+ }
+#endif
- constexpr unsigned int outputWidth = inputWidth;
- constexpr unsigned int outputHeight = inputHeight;
- constexpr unsigned int outputChannels = inputChannels;
- constexpr unsigned int outputBatchSize = inputBatchSize;
+ // Width: 1
+ // Height: 1
+ // Channels: 10
+ // BatchSize: 1
- const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
- armnn::DataType::Float32);
- const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
- armnn::DataType::Float32);
+ const armnn::TensorShape inputOutputShape{ 1, 1, 1, 10 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (1) x Channel (10)
+ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f
+ };
+ const float approxInvL2Norm = 0.050964719f;
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (1) x Channel (10)
+ 1.0f * approxInvL2Norm,
+ 2.0f * approxInvL2Norm,
+ 3.0f * approxInvL2Norm,
+ 4.0f * approxInvL2Norm,
+ 5.0f * approxInvL2Norm,
+ 6.0f * approxInvL2Norm,
+ 7.0f * approxInvL2Norm,
+ 8.0f * approxInvL2Norm,
+ 9.0f * approxInvL2Norm,
+ 10.0f * approxInvL2Norm
+ };
- auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
- // Channel 0
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<float, 4> L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 5
+ // Height: 1
+ // Channels: 2
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 2, 1, 5 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (1) x Width (5)
+ 1.0f, 3.0f, 5.0f, 7.0f, 9.0f,
+
+ // Batch 0, Channel 1, Height (1) x Width (5)
+ 2.0f, 4.0f, 6.0f, 8.0f, 10.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (1) x Width (5)
+ 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
+ 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
+ 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
+ 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
+ 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),
+
+ // Batch 0, Channel 1, Height (1) x Width (5)
+ 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
+ 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
+ 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
+ 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
+ 10.0f * CalcInvL2Norm({ 9.0f, 10.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> L2Normalization2dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 5
+ // Height: 1
+ // Channels: 2
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 1, 5, 2 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (5) x Channel (2)
+ 1.0f, 2.0f,
+ 3.0f, 4.0f,
+ 5.0f, 6.0f,
+ 7.0f, 8.0f,
+ 9.0f, 10.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (5) x Channel (2)
+ 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
+ 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
+ 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
+ 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
+ 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
+ 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
+ 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
+ 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
+ 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),
+ 10.0f * CalcInvL2Norm({ 9.0f, 10.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<float, 4> L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 3
+ // Height: 4
+ // Channels: 2
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 2, 4, 3 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (4) x Width (3)
119.0f, 21.0f, 150.0f,
149.0f, 32.0f, 179.0f,
15.0f, 227.0f, 141.0f,
147.0f, 199.0f, 220.0f,
- // Channel 1
+ // Batch 0, Channel 1, Height (4) x Width (3)
110.0f, 140.0f, 73.0f,
211.0f, 212.0f, 89.0f,
24.0f, 138.0f, 188.0f,
- 162.0f, 12.0f, 161.0f,
- }));
-
- LayerTestResult<float, 4> result(outputTensorInfo);
- result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
+ 162.0f, 12.0f, 161.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (4) x Width (3)
119.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
21.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
150.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
199.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
220.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
+ // Batch 0, Channel 1, Height (4) x Width (3)
110.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
140.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
73.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
188.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
162.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
12.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
- 161.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
- }));
+ 161.0f * CalcInvL2Norm({ 220.0f, 161.0f })
+ };
- std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
- std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
+}
- armnn::L2NormalizationQueueDescriptor descriptor;
- armnn::WorkloadInfo info;
- AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
- AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+LayerTestResult<float, 4> L2Normalization3dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 3
+ // Height: 4
+ // Channels: 2
+ // BatchSize: 1
- std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
+ const armnn::TensorShape inputOutputShape{ 1, 4, 3, 2 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (3) x Channel (2)
+ 119.0f, 110.0f,
+ 21.0f, 140.0f,
+ 150.0f, 73.0f,
+
+ // Batch 0, Height 1, Width (3) x Channel (2)
+ 149.0f, 211.0f,
+ 32.0f, 212.0f,
+ 179.0f, 89.0f,
+
+ // Batch 0, Height 2, Width (3) x Channel (2)
+ 15.0f, 24.0f,
+ 227.0f, 138.0f,
+ 141.0f, 188.0f,
+
+ // Batch 0, Height 3, Width (3) x Channel (2)
+ 147.0f, 162.0f,
+ 199.0f, 12.0f,
+ 220.0f, 161.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (3) x Channel (2)
+ 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
+ 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
+ 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
+ 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
+ 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
+ 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
- inputHandle->Allocate();
- outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+ // Batch 0, Height 1, Width (3) x Channel (2)
+ 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
+ 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
+ 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }),
+ 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }),
+ 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }),
+ 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }),
- workloadFactory.Finalize();
- workload->Execute();
+ // Batch 0, Height 2, Width (3) x Channel (2)
+ 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }),
+ 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }),
+ 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
+ 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
+ 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
+ 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
- CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
- return result;
+ // Batch 0, Height 3, Width (3) x Channel (2)
+ 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
+ 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
+ 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
+ 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
+ 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
+ 161.0f * CalcInvL2Norm({ 220.0f, 161.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
}
LayerTestResult<float, 4> L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory)
{
- constexpr unsigned int inputWidth = 3;
- constexpr unsigned int inputHeight = 4;
- constexpr unsigned int inputChannels = 3;
- constexpr unsigned int inputBatchSize = 2;
-
- constexpr unsigned int outputWidth = inputWidth;
- constexpr unsigned int outputHeight = inputHeight;
- constexpr unsigned int outputChannels = inputChannels;
- constexpr unsigned int outputBatchSize = inputBatchSize;
-
- const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
- armnn::DataType::Float32);
- const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
- armnn::DataType::Float32);
+ // Width: 3
+ // Height: 4
+ // Channels: 3
+ // BatchSize: 2
- auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
- // Batch 0, Channel 0
+ const armnn::TensorShape inputOutputShape{ 2, 3, 4, 3 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (4) x Width (3)
235.0f, 46.0f, 178.0f,
100.0f, 123.0f, 19.0f,
172.0f, 74.0f, 250.0f,
6.0f, 195.0f, 80.0f,
- // Batch 0, Channel 1
+ // Batch 0, Channel 1, Height (4) x Width (3)
113.0f, 95.0f, 202.0f,
77.0f, 114.0f, 71.0f,
122.0f, 246.0f, 166.0f,
82.0f, 28.0f, 37.0f,
- // Batch 0, Channel 2
+ // Batch 0, Channel 2, Height (4) x Width (3)
56.0f, 170.0f, 162.0f,
194.0f, 89.0f, 254.0f,
12.0f, 209.0f, 200.0f,
1.0f, 64.0f, 54.0f,
- // Batch 1, Channel 0
+ // Batch 1, Channel 0, Height (4) x Width (3)
67.0f, 90.0f, 49.0f,
7.0f, 163.0f, 18.0f,
25.0f, 117.0f, 103.0f,
247.0f, 59.0f, 189.0f,
- // Batch 1, Channel 1
+ // Batch 1, Channel 1, Height (4) x Width (3)
239.0f, 104.0f, 199.0f,
17.0f, 124.0f, 153.0f,
222.0f, 217.0f, 75.0f,
32.0f, 126.0f, 21.0f,
- // Batch 1, Channel 2
+ // Batch 1, Channel 2, Height (4) x Width (3)
97.0f, 145.0f, 215.0f,
115.0f, 116.0f, 238.0f,
226.0f, 16.0f, 132.0f,
- 92.0f, 125.0f, 88.0f,
- }));
-
- LayerTestResult<float, 4> result(outputTensorInfo);
- result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
-
- // Batch 0, Channel 0
+ 92.0f, 125.0f, 88.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (4) x Width (3)
235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
- // Batch 0, Channel 1
+ // Batch 0, Channel 1, Height (4) x Width (3)
113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
- // Batch 0, Channel 2
+ // Batch 0, Channel 2, Height (4) x Width (3)
56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
- // Batch 1, Channel 0
+ // Batch 1, Channel 0, Height (4) x Width (3)
67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
- // Batch 1, Channel 1
+ // Batch 1, Channel 1, Height (4) x Width (3)
239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
- // Batch 1, Channel 2
+ // Batch 1, Channel 2, Height (4) x Width (3)
97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
- 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
- }));
+ 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f })
+ };
- std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
- std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
+}
- armnn::L2NormalizationQueueDescriptor descriptor;
- armnn::WorkloadInfo info;
- AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
- AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+LayerTestResult<float, 4> L2Normalization4dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 3
+ // Height: 4
+ // Channels: 3
+ // BatchSize: 2
- std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
+ const armnn::TensorShape inputOutputShape{ 2, 4, 3, 3 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (3) x Channel (3)
+ 235.0f, 113.0f, 56.0f,
+ 46.0f, 95.0f, 170.0f,
+ 178.0f, 202.0f, 162.0f,
+
+ // Batch 0, Height 1, Width (3) x Channel (3)
+ 100.0f, 77.0f, 194.0f,
+ 123.0f, 114.0f, 89.0f,
+ 19.0f, 71.0f, 254.0f,
+
+ // Batch 0, Height 2, Width (3) x Channel (3)
+ 172.0f, 122.0f, 12.0f,
+ 74.0f, 246.0f, 209.0f,
+ 250.0f, 166.0f, 200.0f,
+
+ // Batch 0, Height 3, Width (3) x Channel (3)
+ 6.0f, 82.0f, 1.0f,
+ 195.0f, 28.0f, 64.0f,
+ 80.0f, 37.0f, 54.0f,
+
+ // Batch 1, Height 0, Width (3) x Channel (3)
+ 67.0f, 239.0f, 97.0f,
+ 90.0f, 104.0f, 145.0f,
+ 49.0f, 199.0f, 215.0f,
+
+ // Batch 1, Height 1, Width (3) x Channel (3)
+ 7.0f, 17.0f, 115.0f,
+ 163.0f, 124.0f, 116.0f,
+ 18.0f, 153.0f, 238.0f,
+
+ // Batch 1, Height 2, Width (3) x Channel (3)
+ 25.0f, 222.0f, 226.0f,
+ 117.0f, 217.0f, 16.0f,
+ 103.0f, 75.0f, 132.0f,
+
+ // Batch 1, Height 3, Width (3) x Channel (3)
+ 247.0f, 32.0f, 92.0f,
+ 59.0f, 126.0f, 125.0f,
+ 189.0f, 21.0f, 88.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (3) x Channel (3)
+ 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
+ 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
+ 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
- inputHandle->Allocate();
- outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+ // Batch 0, Height 1, Width (3) x Channel (3)
+ 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
+ 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
+ 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
- workloadFactory.Finalize();
- workload->Execute();
+ // Batch 0, Height 2, Width (3) x Channel (3)
+ 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
+ 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
+ 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
- CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
- return result;
+ // Batch 0, Height 3, Width (3) x Channel (3)
+ 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+ 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+ 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+
+ // Batch 1, Height 0, Width (3) x Channel (3)
+ 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+ 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+ 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+
+ // Batch 1, Height 1, Width (3) x Channel (3)
+ 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+ 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+ 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+
+ // Batch 1, Height 2, Width (3) x Channel (3)
+ 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+ 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+ 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+
+ // Batch 1, Height 3, Width (3) x Channel (3)
+ 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
+ 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
+ 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
}
template <typename T>
projects / platform / upstream / armnn.git / commitdiff