| // |
| // Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ConvolutionTestHelper.hpp" |
| |
| #include <armnn_delegate.hpp> |
| |
| #include <flatbuffers/flatbuffers.h> |
| #include <tensorflow/lite/interpreter.h> |
| #include <tensorflow/lite/kernels/register.h> |
| #include <tensorflow/lite/model.h> |
| |
| #include <doctest/doctest.h> |
| |
| namespace armnnDelegate |
| { |
| |
| // Conv3d is currently only supports Float32 inputs, filter, bias and outputs in TFLite. |
| // Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5. |
| #if defined(ARMNN_POST_TFLITE_2_5) |
| |
| // Create a vector from 0 to size divided to create smaller floating point values. |
| template <typename T> |
| std::vector<T> CreateFloatData(int32_t size, float divisor) |
| { |
| std::vector<float> data; |
| for (int32_t i = 0; i < size; ++i) |
| { |
| float value = static_cast<float>(i); |
| data.push_back(value/divisor); |
| } |
| return data; |
| } |
| |
| void Conv3DWithBiasesSimpleWithPaddingFp32Test() |
| { |
| // Set input data |
| std::vector<int32_t> inputShape { 1, 2, 2, 2, 1 }; |
| std::vector<int32_t> filterShape { 2, 2, 2, 1, 1 }; |
| std::vector<int32_t> biasShape { 1 }; |
| std::vector<int32_t> outputShape { 1, 2, 2, 2, 1 }; |
| |
| static std::vector<float> inputValues = |
| { |
| 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f |
| }; |
| |
| std::vector<float> filterValues = |
| { |
| 2.f,1.f, 1.f,0.f, 0.f,1.f, 1.f,1.f |
| }; |
| |
| std::vector<float> biasValues = { 5.f }; |
| |
| std::vector<float> expectedOutputValues = |
| { |
| 33.f, 21.f, 23.f, 13.f, 28.f, 25.f, 27.f, 21.f |
| }; |
| |
| Convolution3dTest<float>(tflite::BuiltinOperator_CONV_3D, |
| ::tflite::TensorType_FLOAT32, |
| { 1, 1, 1 }, // strideX, strideY, strideZ |
| { 1, 1, 1 }, // dilationX, dilationY, dilationZ |
| tflite::Padding_SAME, |
| tflite::ActivationFunctionType_NONE, |
| inputShape, |
| filterShape, |
| outputShape, |
| inputValues, |
| filterValues, |
| expectedOutputValues, |
| biasShape, |
| biasValues); |
| } |
| |
| void Conv3DWithBiasesStridesFp32Test() |
| { |
| std::vector<int32_t> inputShape { 1, 3, 10, 10, 1 }; |
| std::vector<int32_t> filterShape { 3, 5, 5, 1, 1 }; |
| std::vector<int32_t> biasShape { 1 }; |
| std::vector<int32_t> outputShape { 1, 1, 3, 3, 1 }; |
| |
| std::vector<float> inputValues = CreateFloatData<float>(300, 1.0f); |
| |
| std::vector<float> filterValues = |
| { |
| 1.f, 1.f, 1.f, 1.f, 1.f, |
| 1.f, 1.f, 1.f, 1.f, 1.f, |
| 1.f, 1.f, 1.f, 1.f, 1.f, |
| 1.f, 1.f, 1.f, 1.f, 1.f, |
| 1.f, 1.f, 1.f, 1.f, 1.f, |
| |
| 0.f, 0.f, 0.f, 0.f, 0.f, |
| 0.f, 0.f, 0.f, 0.f, 0.f, |
| 0.f, 0.f, 0.f, 0.f, 0.f, |
| 0.f, 0.f, 0.f, 0.f, 0.f, |
| 0.f, 0.f, 0.f, 0.f, 0.f, |
| |
| 2.f, 2.f, 2.f, 2.f, 2.f, |
| 2.f, 2.f, 2.f, 2.f, 2.f, |
| 2.f, 2.f, 2.f, 2.f, 2.f, |
| 2.f, 2.f, 2.f, 2.f, 2.f, |
| 2.f, 2.f, 2.f, 2.f, 2.f |
| }; |
| |
| std::vector<float> biasValues = { 10.f }; |
| |
| std::vector<float> expectedOutputValues = |
| { |
| 11660.f, 11810.f, 11960.f, |
| |
| 13160.f, 13310.f, 13460.f, |
| |
| 14660.f, 14810.f, 14960.f |
| }; |
| |
| Convolution3dTest<float>(tflite::BuiltinOperator_CONV_3D, |
| ::tflite::TensorType_FLOAT32, |
| { 2, 2, 2 }, // strideX, strideY, strideZ |
| { 1, 1, 1 }, // dilationX, dilationY, dilationZ |
| tflite::Padding_VALID, |
| tflite::ActivationFunctionType_NONE, |
| inputShape, |
| filterShape, |
| outputShape, |
| inputValues, |
| filterValues, |
| expectedOutputValues, |
| biasShape, |
| biasValues); |
| } |
| |
| |
| void Conv3DWithBiasesDilationFp32Test(std::vector<armnn::BackendId>& backends) |
| { |
| std::vector<int32_t> inputShape { 1, 5, 5, 5, 2 }; |
| std::vector<int32_t> filterShape { 2, 2, 2, 2, 2 }; |
| std::vector<int32_t> biasShape { 2 }; |
| std::vector<int32_t> outputShape { 1, 2, 2, 2, 2 }; |
| |
| std::vector<float> inputValues = CreateFloatData<float>(250, 1.0f); |
| |
| std::vector<float> filterValues = |
| { |
| -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, 1.f, 1.f, 1.f, -1.f, -1.f, |
| 1.f, 1.f, -1.f, 1.f, -1.f, 1.f, -1.f, 1.f, -1.f, -1.f, -1.f, 1.f, -1.f, 1.f, -1.f, 1.f, |
| }; |
| |
| std::vector<float> biasValues = { 0.f, 2.f }; |
| |
| // Since the dilation rate is 3 this will dilate the kernel to be 4x4, |
| // therefore the output will be 2x2 |
| std::vector<float> expectedOutputValues = |
| { |
| -1124.f, 976.f, |
| -1148.f, 980.f, |
| |
| -1244.f, 996.f, |
| -1268.f, 1000.f, |
| |
| -1724.f, 1076.f, |
| -1748.f, 1080.f, |
| |
| -1844.f, 1096.f, |
| -1868.f, 1100.f |
| }; |
| |
| Convolution3dTest<float>(tflite::BuiltinOperator_CONV_3D, |
| ::tflite::TensorType_FLOAT32, |
| { 1, 1, 1 }, // strideX, strideY, strideZ |
| { 3, 3, 3 }, // dilationX, dilationY, dilationZ |
| tflite::Padding_VALID, |
| tflite::ActivationFunctionType_NONE, |
| inputShape, |
| filterShape, |
| outputShape, |
| inputValues, |
| filterValues, |
| expectedOutputValues, |
| biasShape, |
| biasValues, |
| {1.0f}, |
| {0}, |
| {1.0f}, |
| {0}, |
| 2.0f, |
| 0, |
| 1.0f, |
| 0, |
| 1, |
| 3, |
| backends); |
| } |
| |
| void Conv3DFp32SmallTest() |
| { |
| std::vector<int32_t> inputShape { 1, 3, 10, 10, 1 }; |
| std::vector<int32_t> filterShape { 3, 3, 3, 1, 1 }; |
| std::vector<int32_t> biasShape { 1 }; |
| std::vector<int32_t> outputShape { 1, 1, 4, 4, 1 }; |
| |
| std::vector<float> inputValues = CreateFloatData<float>(300, 100.0f); |
| |
| std::vector<float> filterValues = |
| { |
| 0.125977f, 0.150391f, 0.101562f, |
| 0.0585938f, 0.0864258f, 0.043457f, |
| 0.034668f, 0.0322266f, 0.0385742f, |
| |
| 0.125977f, 0.150391f, -0.101562f, |
| -0.0585938f,-0.0864258f,-0.043457f, |
| -0.0104630f, 0.0154114f, 0.0013768f, |
| |
| 0.0344238f, 0.035644f, 0.0495605f, |
| 0.0683594f, 0.099121f, -0.0461426f, |
| -0.0996094f,-0.126953f, -0.043457f, |
| }; |
| |
| std::vector<float> biasValues = { 0 }; |
| |
| std::vector<float> expectedOutputValues = |
| { |
| -0.08156067f, -0.06891209f, -0.05589598f, -0.04310101f, |
| 0.04584253f, 0.05855697f, 0.07129729f, 0.08325434f, |
| 0.17304349f, 0.18521416f, 0.19818866f, 0.21096253f, |
| 0.29965734f, 0.312698f, 0.32547557f, 0.33818722f |
| }; |
| |
| Convolution3dTest<float>(tflite::BuiltinOperator_CONV_3D, |
| ::tflite::TensorType_FLOAT32, |
| { 2, 2, 2 }, // strideX, strideY, strideZ |
| { 1, 1, 1 }, // dilationX, dilationY, dilationZ |
| tflite::Padding_VALID, |
| tflite::ActivationFunctionType_NONE, |
| inputShape, |
| filterShape, |
| outputShape, |
| inputValues, |
| filterValues, |
| expectedOutputValues, |
| biasShape, |
| biasValues); |
| } |
| |
| TEST_SUITE("Convolution3dTest_CpuRefTests") |
| { |
| |
| TEST_CASE ("Conv3DWithBiasesSimpleWithPadding_Fp32_Test") |
| { |
| Conv3DWithBiasesSimpleWithPaddingFp32Test(); |
| } |
| |
| TEST_CASE ("Conv3DWithBiasesStrides_Fp32_Test") |
| { |
| Conv3DWithBiasesStridesFp32Test(); |
| } |
| |
| TEST_CASE ("Conv3DWithBiasesDilation_Fp32_CpuRef_Test") |
| { |
| // Known to only work on CpuRef. |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| Conv3DWithBiasesDilationFp32Test(backends); |
| } |
| |
| TEST_CASE ("Conv3DFp32Small_Fp32_Test") |
| { |
| Conv3DFp32SmallTest(); |
| } |
| |
| } //End of TEST_SUITE("Convolution3dTest_Tests") |
| |
| #endif |
| |
| } // namespace armnnDelegate |