diff options
Diffstat (limited to 'src/armnn/backends/test/LayerTests.cpp')
| -rw-r--r-- | src/armnn/backends/test/LayerTests.cpp | 166 |
1 files changed, 109 insertions, 57 deletions
diff --git a/src/armnn/backends/test/LayerTests.cpp b/src/armnn/backends/test/LayerTests.cpp index a10e4bd7a0..8039ffb9b1 100644 --- a/src/armnn/backends/test/LayerTests.cpp +++ b/src/armnn/backends/test/LayerTests.cpp @@ -35,8 +35,11 @@ #include "SoftmaxTestImpl.hpp" #include "NormTestImpl.hpp" #include "PermuteTestImpl.hpp" +#include "LstmTestImpl.hpp" +#include "ConvertFp16ToFp32TestImpl.hpp" +#include "ConvertFp32ToFp16TestImpl.hpp" -// 3-channel 16x8 image used as common input data for a number of Conv2d tests +// 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, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, @@ -64,10 +67,10 @@ static std::vector<float> ConvInput3x8x16({ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }); -// 2-channel bias used by a number of Conv2d tests +// 2-channel bias used by a number of Conv2d tests. static std::vector<float> Bias2({0, 2}); -// Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled +// Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled. template<typename T> boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale, int32_t qOffset) { @@ -89,11 +92,11 @@ LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& int32_t qOffset, bool biasEnabled) { - // Use common single-batch 3-channel 16x8 image + // Use common single-batch 3-channel 16x8 image. armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>()); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16)); - // Use a 2-element batch with 3-channel 3x5 kernels + // Use a 2-element batch with 3-channel 3x5 kernels. armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType<T>()); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -135,7 +138,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& 0, 0, 0 }))); - // Expected output is 2 batch elements of a 1-channel 14x4 image + // Expected output is 2 batch elements of a 1-channel 14x4 image. armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType<T>()); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -167,13 +170,13 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& int32_t qOffset, bool biasEnabled) { - // Use a 3x3 kernel, which exercises ArmCompute's direct convolution path + // Use a 3x3 kernel, which exercises ArmCompute's direct convolution path. - // Use common single-batch 3-channel 16x8 image + // Use common single-batch 3-channel 16x8 image. armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>()); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16)); - // Use a 2-element batch of 3-channel 3x3 kernels + // Use a 2-element batch of 3-channel 3x3 kernels. armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType<T>()); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -203,7 +206,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& 0, 0, 0 }))); - // Expected output is 1 batch of a 2-channel 14x6 image + // Expected output is 1 batch of a 2-channel 14x6 image. armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType<T>()); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -261,7 +264,7 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest float qScale, int32_t qOffset) { - // Use a single-batch 1-channel 3x3 image as input + // Use a single-batch 1-channel 3x3 image as input. armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType<T>()); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -270,7 +273,7 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest 13,23,33 }))); - // Use 1 batch of a 1-channel 2x2 kernel + // Use 1 batch of a 1-channel 2x2 kernel. armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType<T>()); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -278,7 +281,7 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest -12,-22, }))); -// Expected output is 1 batch of a 1-channel 6x8 image +// Expected output is 1 batch of a 1-channel 6x8 image. // Manually calculated like this: //[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..] //[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..] @@ -307,10 +310,10 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest expectedOutput, qScale, qOffset, - 1, // padding left - 2, // padding top - 3, // padding right - 4); // padding bottom + 1, // Padding left. + 2, // Padding top. + 3, // Padding right. + 4); // Padding bottom. } template<typename T> @@ -318,7 +321,7 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWor float qScale, int32_t qOffset) { - // Use a single-batch 1-channel 5x5 image as input + // Use a single-batch 1-channel 5x5 image as input. armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>()); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -329,7 +332,7 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWor 15,25,35,45,55, }))); - // Use 1 batch of a 1-channel 4x4 kernel + // Use 1 batch of a 1-channel 4x4 kernel. armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -339,7 +342,7 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWor -14,-24,-34,-44, }))); - // Expected output is 1 batch of a 1-channel 5x5 image + // Expected output is 1 batch of a 1-channel 5x5 image. armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>()); std::vector<T> myVec(outputDesc.GetNumElements(), 0); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( @@ -358,10 +361,10 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWor expectedOutput, qScale, qOffset, - 1, // padding left - 1, // padding top - 2, // padding right - 2); // padding bottom + 1, // Padding left. + 1, // Padding top. + 2, // Padding right. + 2); // Padding bottom. } template<typename T> @@ -370,7 +373,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloa int32_t qOffset, bool biasEnabled) { - // Use a single-batch 2-channel 5x5 image as input + // Use a single-batch 2-channel 5x5 image as input. armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>()); auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { @@ -387,7 +390,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloa 45, 46, 47, 48, 49 }))); - // Use a depth multiplier of 1 on a 2-channel 4x4 kernel + // Use a depth multiplier of 1 on a 2-channel 4x4 kernel. armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType<T>()); auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { @@ -402,8 +405,8 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloa 4, 3, 2, 1 }))); - // Expected output is 1 batch of a 2-channel 5x5 image - // calculated using the python tensorflow library with strideX=1, strideY=1 + // Expected output is 1 batch of a 2-channel 5x5 image. + // Calculated using the python tensorflow library with strideX=1, strideY=1. armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>()); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { @@ -426,10 +429,10 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloa expectedOutput, qScale, qOffset, - 1, // padding left - 1, // padding top - 2, // padding right - 2, // padding bottom + 1, // Padding left. + 1, // Padding top. + 2, // Padding right. + 2, // Padding bottom. 1, // strideX 1); // strideY } @@ -569,6 +572,55 @@ LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test(armnn::IWorkloadFactory& wo return CopyViaSplitterTestImpl<uint8_t>(workloadFactory, 1.0f, 0); } +LayerTestResult<float, 2> LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest( + armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType<float>()); + boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( + { 2., 3., 3., 4. })); + + armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType<float>()); + boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( + {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, + -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f})); + return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); +} + +LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest( + armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType<float>()); + boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( + {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, + 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f})); + + armnn::TensorInfo outputDesc({ 2, 16 }, armnn::GetDataType<float>()); + boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( + {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, + -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, + -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, + 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, + -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, + 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, + 0.02168f})); + return LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(workloadFactory, input, expectedOutput); +} + +LayerTestResult<float, 2> LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType<float>()); + boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( + {2., 3., 3., 4.})); + + + armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType<float>()); + boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( + {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, + -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}})); + + return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); +} + LayerTestResult<float,3> MergerTest(armnn::IWorkloadFactory& workloadFactory) { unsigned int outputWidth = 3; @@ -583,7 +635,7 @@ LayerTestResult<float,3> MergerTest(armnn::IWorkloadFactory& workloadFactory) unsigned int inputHeight2 = 6; unsigned int inputChannels2 = 1; - // Define the tensor descriptors + // Define the tensor descriptors. armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32); armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32); armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32); @@ -644,10 +696,10 @@ LayerTestResult<float,3> MergerTest(armnn::IWorkloadFactory& workloadFactory) }) ); - std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //extent of the window is defined by size of input[0] + std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); - std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //extent of the window is defined by size of input[1] + std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -1350,7 +1402,7 @@ armnn::OriginsDescriptor CreateMergerDescriptorForConcatenation( // // Concatenation is only supported for N and C dimensions for NCHW. In case of -// <4 dimensions we need to make sure that the concat dimensions is at least +// <4 dimensions we need to make sure that the concat dimensions are at least // the 3rd slowest iterating one. // @@ -1362,8 +1414,8 @@ bool NeedPermuteForConcat( // same number of dimensions. unsigned int nDimensions = 0; - // determine the number of dimensions as well as sanity check them - // agains test implementation issues + // Determine the number of dimensions as well as sanity check them + // agains test implementation issues. for (auto && tensorInfo : inputTensorInfos) { if (!nDimensions) @@ -1464,7 +1516,7 @@ void PermuteInputsForConcat( { numDims = tensorInfo.GetShape().GetNumDimensions(); Generate3dPermuteVectorForConcat(numDims, concatDim, permutations); - // store the reverese permutation + // Store the reverese permutation. permuteVector = permutations.second; BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity), "Test logic error, we don't need permutation, so we shouldn't arrive here"); @@ -1499,7 +1551,7 @@ void PermuteInputsForConcat( // // This is the pair of PermuteInputsForConcat(...) which permutes back -// the output of the concatenation so we can check against an expected +// the output of the concatenation so we can check it against an expected // output. // template <typename T> @@ -1553,14 +1605,14 @@ void Concatenate(armnn::IWorkloadFactory& workloadFactory, armnn::MergerQueueDescriptor queueDescriptor; - // save a copy of the parameters which we might need to change + // Saves a copy of the parameters which we might need to change. std::vector<armnn::TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); std::vector<T *> inputs = inputsOrig; armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig; armnn::PermutationVector permuteVector{0, 1, 2}; - // hold and automatically release memory for the reshaped input data + // Holds and automatically releases memory for the reshaped input data. std::vector<std::vector<T>> tmpInputDataStorage; const size_t inputCount = inputTensorInfos.size(); @@ -1571,7 +1623,7 @@ void Concatenate(armnn::IWorkloadFactory& workloadFactory, { // // We need to permute the inputs, because concatenation along - // the requested axis is not supported + // the requested axis is not supported. // PermuteInputsForConcat<T>(workloadFactory, inputTensorInfos, @@ -2641,7 +2693,7 @@ LayerTestResult<float, 4> SimpleResizeBilinearTest(armnn::IWorkloadFactory& work // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, // then figures out the interpolants and weights. Note this is different to projecting the centre of the - // output texel - and thus we'll expect the output 1x1 matrix to contain as its single element the value + // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting // the centre). LayerTestResult<float, 4> result(outputTensorInfo); @@ -3367,12 +3419,12 @@ LayerTestResult<uint8_t, 3> MergerUint8Test(armnn::IWorkloadFactory& workloadFac unsigned int inputHeight2 = 6; unsigned int inputChannels2 = 1; - // Define the tensor descriptors + // Defines the tensor descriptors. armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8); armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8); armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8); - // Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize + // Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize them. const float scale = 0.13497836f; const int32_t offset = -7; @@ -3439,10 +3491,10 @@ LayerTestResult<uint8_t, 3> MergerUint8Test(armnn::IWorkloadFactory& workloadFac }) ); - std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //extent of the window is defined by size of input[0] + std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); - std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //extent of the window is defined by size of input[1] + std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); @@ -3513,21 +3565,21 @@ LayerTestResult<uint8_t, 4> AdditionUint8Test(armnn::IWorkloadFactory& workloadF outputTensorInfo.SetQuantizationScale(scale); outputTensorInfo.SetQuantizationOffset(offset); - // See dequantized values to the right + // See dequantized values to the right. auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>( { 63, 35, 77, 70, 56, 112, // 420, 224, 518, 469, 371, 763 203, 28, 252, 168, 245, 91 // 1400, 175, 1743, 1155, 1694, 616 })); - // See dequantized values to the right + // See dequantized values to the right. auto input2 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>( { 21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449 126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861 })); - // See dequantized values to the right + // See dequantized values to the right. LayerTestResult<uint8_t, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>( { @@ -3633,19 +3685,19 @@ LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& wor unsigned int width = 3; const unsigned int shape[] = { batchSize, channels, height, width }; - // See dequantized values to the right + // See dequantized values to the right. std::vector<uint8_t> input0({ 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440, 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120 }); - // See dequantized values to the right + // See dequantized values to the right. std::vector<uint8_t> input1({ 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747, 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297 }); - // See dequantized values to the right + // See dequantized values to the right. std::vector<uint8_t> output( { 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680, @@ -3663,7 +3715,7 @@ LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& wor -2, shape, output, - 1366.255f, // Scale/offset chosen to have output values out of range + 1366.255f, // Scale/offset chosen to have output values out of range. -5); } @@ -3813,7 +3865,7 @@ LayerTestResult<uint8_t, 4> SimpleResizeBilinearUint8Test(armnn::IWorkloadFactor // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, // then figures out the interpolants and weights. Note this is different to projecting the centre of the - // output texel - and thus we'll expect the output 1x1 matrix to contain as its single element the value + // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting // the centre). LayerTestResult<uint8_t, 4> result(outputTensorInfo); @@ -4314,4 +4366,4 @@ LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& w LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory) { return PermuteFloat32ValueSet3TestCommon(workloadFactory); -}; +};
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