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-rw-r--r--delegate/src/test/Convolution2dTest.cpp489
1 files changed, 0 insertions, 489 deletions
diff --git a/delegate/src/test/Convolution2dTest.cpp b/delegate/src/test/Convolution2dTest.cpp
deleted file mode 100644
index 10510792a1..0000000000
--- a/delegate/src/test/Convolution2dTest.cpp
+++ /dev/null
@@ -1,489 +0,0 @@
-//
-// Copyright © 2020,2023 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 <tensorflow/lite/schema/schema_generated.h>
-#include <tensorflow/lite/version.h>
-
-#include <doctest/doctest.h>
-
-namespace armnnDelegate
-{
-
-void Conv2DWithBiasesFp32Test(std::vector<armnn::BackendId>& backends)
-{
- // Set input data
- std::vector<int32_t> inputShape { 1, 5, 5, 1 };
- std::vector<int32_t> filterShape { 1, 3, 3, 1 };
- std::vector<int32_t> biasShape { 1 };
- std::vector<int32_t> outputShape { 1, 3, 3, 1 };
-
- static std::vector<float> inputValues =
- {
- 1, 5, 2, 3, 5,
- 8, 7, 3, 6, 3,
- 3, 3, 9, 1, 9,
- 4, 1, 8, 1, 3,
- 6, 8, 1, 9, 2
- };
-
- std::vector<float> filterValues =
- {
- 4, 5, 6,
- 0, 0, 0,
- 3, 2, 1
- };
-
- std::vector<float> biasValues = { 0 };
-
- std::vector<float> expectedOutputValues =
- {
- 23, 33, 24,
- 91, 99, 48,
- 26, 50, 19
- };
-
- tflite::Padding padding = tflite::Padding_SAME;
-
- ConvolutionTest<float>(tflite::BuiltinOperator_CONV_2D,
- ::tflite::TensorType_FLOAT32,
- 2, // strideX
- 2, // strideY
- 1, // dilationX
- 1, // dilationY
- padding,
- tflite::ActivationFunctionType_NONE,
- backends,
- inputShape,
- filterShape,
- outputShape,
- inputValues,
- filterValues,
- expectedOutputValues,
- biasShape,
- biasValues);
-}
-
-void Conv2DWithBiasesInt8Test(std::vector<armnn::BackendId>& backends)
-{
- // Set input data
- std::vector<int32_t> inputShape { 1, 2, 2, 1 };
- std::vector<int32_t> filterShape { 1, 2, 2, 1 };
- std::vector<int32_t> biasShape { 1 };
- std::vector<int32_t> outputShape { 1, 2, 2, 1 };
-
- static std::vector<int8_t> inputValues = { 1, 2, 3, 4 };
-
- std::vector<int8_t> filterValues = { 2, 1, 0, 6 };
-
- std::vector<int32_t> biasValues = { 10 };
-
- std::vector<int8_t> expectedOutputValues =
- {
- (1 * 2 + 2 * 1 + 3 * 0 + 4 * 6 + 10) / 2, // 19
- (2 * 2 + 0 * 1 + 4 * 0 + 0 * 6 + 10) / 2, // 7
- (3 * 2 + 4 * 1 + 0 * 0 + 0 * 6 + 10) / 2, // 10
- (4 * 2 + 0 * 1 + 0 * 0 + 0 * 6 + 10) / 2, // 9
- };
-
- tflite::Padding padding = tflite::Padding_SAME;
-
- ConvolutionTest<int8_t, int32_t>(tflite::BuiltinOperator_CONV_2D,
- ::tflite::TensorType_INT8,
- 1, // strideX
- 1, // strideY
- 1, // dilationX
- 1, // dilationY
- padding,
- tflite::ActivationFunctionType_NONE,
- backends,
- inputShape,
- filterShape,
- outputShape,
- inputValues,
- filterValues,
- expectedOutputValues,
- biasShape,
- biasValues);
-}
-
-void Conv2DWithBiasesReluUint8Test(std::vector<armnn::BackendId>& backends)
-{
- // Set input data
- std::vector<int32_t> inputShape { 1, 2, 2, 1 };
- std::vector<int32_t> filterShape { 1, 2, 2, 1 };
- std::vector<int32_t> biasShape { 1 };
- std::vector<int32_t> outputShape { 1, 2, 2, 1 };
-
- static std::vector<uint8_t> inputValues = { 1, 2, 4, 8 };
-
- std::vector<uint8_t> filterValues = { 2, 1, 0, 6 };
-
- std::vector<int32_t> biasValues = { 16 };
-
- // factors to consider:
- // - the filter zero point is non zero, hence the (x-fz)
- // - the output scale is 2 hence the /2
- // - output zero point is non zero, hence the +outZero
- // - RELU cuts negative values and then we add the output zero point
- uint8_t bias = 16;
- uint8_t outZero = 20;
- uint8_t fz = 4; // filter zero point
-
- std::vector<uint8_t> expectedOutputValues =
- {
- std::max(outZero, static_cast<uint8_t>((1*(2-fz) + 2*(1-fz) + 4*(0-fz) + 8*(6-fz) + bias)/2 + outZero)),
- std::max(outZero, static_cast<uint8_t>((2*(2-fz) + 0*(1-fz) + 8*(0-fz) + 0*(6-fz) + bias)/2 + outZero)),
- std::max(outZero, static_cast<uint8_t>((4*(2-fz) + 8*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero)),
- std::max(outZero, static_cast<uint8_t>((8*(2-fz) + 0*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero))
- };
-
- tflite::Padding padding = tflite::Padding_SAME;
-
- ConvolutionTest<uint8_t, int32_t>(tflite::BuiltinOperator_CONV_2D,
- ::tflite::TensorType_UINT8,
- 1, // strideX
- 1, // strideY
- 1, // dilationX
- 1, // dilationY
- padding,
- tflite::ActivationFunctionType_RELU,
- backends,
- inputShape,
- filterShape,
- outputShape,
- inputValues,
- filterValues,
- expectedOutputValues,
- biasShape,
- biasValues,
- {1.0f}, // biasScale
- {0}, // biasOffset
- {1.0f}, // filterScale
- {4}, // filterOffsets
- 2, // output scale
- 20); // output offset
-}
-
-void Conv2DWithBiasesRelu6Uint8Test(std::vector<armnn::BackendId>& backends)
-{
- // Set input data
- std::vector<int32_t> inputShape { 1, 2, 2, 1 };
- std::vector<int32_t> filterShape { 1, 2, 2, 1 };
- std::vector<int32_t> biasShape { 1 };
- std::vector<int32_t> outputShape { 1, 2, 2, 1 };
-
- static std::vector<uint8_t> inputValues = { 1, 2, 4, 1 };
-
- std::vector<uint8_t> filterValues = { 2, 1, 0, 6 };
-
- std::vector<int32_t> biasValues = { 0 };
-
- // factors to consider:
- // - the output scale is 2 hence the /2
- // - RELU6 cuts output values at +6
- uint8_t relu6Min = 6 / 2; // divide by output scale
-
- std::vector<uint8_t> expectedOutputValues =
- {
- std::min(relu6Min, static_cast<uint8_t>((1 * 2 + 2 * 1 + 4 * 0 + 1 * 6) / 2)),
- std::min(relu6Min, static_cast<uint8_t>((2 * 2 + 0 * 1 + 1 * 0 + 0 * 6) / 2)),
- std::min(relu6Min, static_cast<uint8_t>((4 * 2 + 1 * 1 + 0 * 0 + 0 * 6) / 2)),
- std::min(relu6Min, static_cast<uint8_t>((1 * 2 + 0 * 1 + 0 * 0 + 0 * 6) / 2))
- };
-
- tflite::Padding padding = tflite::Padding_SAME;
-
- ConvolutionTest<uint8_t, int32_t>(tflite::BuiltinOperator_CONV_2D,
- ::tflite::TensorType_UINT8,
- 1, // strideX
- 1, // strideY
- 1, // dilationX
- 1, // dilationY
- padding,
- tflite::ActivationFunctionType_RELU6,
- backends,
- inputShape,
- filterShape,
- outputShape,
- inputValues,
- filterValues,
- expectedOutputValues,
- biasShape,
- biasValues);
-}
-
-
-void Conv2DPerChannelInt8Test(std::vector<armnn::BackendId>& backends)
-{
- // Set input data
- std::vector<int32_t> inputShape { 1,4,4,2 };
- std::vector<int32_t> filterShape { 4,2,2,2 };
- std::vector<int32_t> biasShape { 4 };
- std::vector<int32_t> outputShape { 1,4,4,4 };
-
- static std::vector<int8_t> inputValues =
- {
- -11, 40,-26, 11,-28, 8, 0, -8,
- -10, 34, 47, 0,-33,-14, 28, 35,
- 6,-28,-26, 8, 13, 33,-31,-41,
- 31,-20,-31,-16, 8,-18,-44, 0
- };
-
- std::vector<float> filterScales = { 1.858268, 2.0, 1.992126, 1.905512 };
- int32_t filterQuantizationDim = 0;
- std::vector<int8_t> filterValues =
- {
- 13,-44, 5,-14, 21,-45, 36,-25,
- -42, -2, 24,-30,-31, 35, 43,-30,
- -20, -5, 25, 17, 18, 20, 4,-46,
- -49, 9, -3,-20, 46, 5, 7,-15
- };
-
- std::vector<int32_t> biasValues = { 0,0,0,0 };
- std::vector<float> biasScales = { 0.721445, 0.7764700055, 0.773414, 0.739787 };
-
- std::vector<int8_t> expectedOutputValues =
- {
- -1, 9, 3, 5, 1, -1, 5, 9,
- 2, 7, -1, 2, 2, 4, 5, 6,
- 1, 1, 4, 4, 2, 0, -4, -3,
- 0, 6, 12, 6, 3, 0, -1, -2,
- 7, -4, 4, 4, 3, 6, 6, 2,
- 0, -3, -1, 4, 4, 8, 3, 1,
- 5, 0, 0, 1, 4, 7, 4, 6,
- 4, 0, 1, 2, 2, 7, 5, 7
- };
- float outputQuantScale = 401.960785f;
- int outputQuantOffset = 3;
- float inputQuantScale = 0.388235f;
- int inputQuantOffset = 1;
-
- tflite::Padding padding = tflite::Padding_SAME;
-
- ConvolutionTest<int8_t, int32_t>(tflite::BuiltinOperator_CONV_2D,
- ::tflite::TensorType_INT8,
- 1, // strideX
- 1, // strideY
- 1, // dilationX
- 1, // dilationY
- padding,
- tflite::ActivationFunctionType_NONE,
- backends,
- inputShape,
- filterShape,
- outputShape,
- inputValues,
- filterValues,
- expectedOutputValues,
- biasShape,
- biasValues,
- biasScales,
- {0,0,0,0},
- filterScales,
- {0,0,0,0},
- outputQuantScale,
- outputQuantOffset,
- inputQuantScale,
- inputQuantOffset,
- 1, // depth_multiplier is ignored for conv2d value doesn't matter
- filterQuantizationDim);
-}
-
-TEST_SUITE("Convolution2dTest_CpuRefTests")
-{
-
-TEST_CASE ("Conv2DWithBiases_Fp32_CpuRef_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef};
- Conv2DWithBiasesFp32Test(backends);
-}
-
-TEST_CASE ("Conv2DWithBiases_Int8_CpuRef_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef};
- Conv2DWithBiasesInt8Test(backends);
-}
-
-TEST_CASE ("Conv2DPerChannel_Int8_CpuRef_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef};
- Conv2DPerChannelInt8Test(backends);
-}
-
-} //End of TEST_SUITE("Convolution2dTest_CpuRef")
-
-TEST_SUITE("Convolution2dTest_CpuAccTests")
-{
-
-TEST_CASE ("Conv2DWithBiases_Fp32_CpuAcc_Test")
-{
-std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc};
-Conv2DWithBiasesFp32Test(backends);
-}
-
-TEST_CASE ("Conv2DWithBiases_Int8_CpuAcc_Test")
-{
-std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc};
-Conv2DWithBiasesInt8Test(backends);
-}
-
-TEST_CASE ("Conv2DPerChannel_Int8_CpuAcc_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc};
- Conv2DPerChannelInt8Test(backends);
-}
-
-} //End of TEST_SUITE("Convolution2dTest_CpuAcc")
-
-TEST_SUITE("Convolution2dTest_GpuAccTests")
-{
-
-TEST_CASE ("Conv2DWithBiases_Fp32_GpuAcc_Test")
-{
-std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc};
-Conv2DWithBiasesFp32Test(backends);
-}
-
-TEST_CASE ("Conv2DWithBiases_Int8_GpuAcc_Test")
-{
-std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc};
-Conv2DWithBiasesInt8Test(backends);
-}
-
-TEST_CASE ("Conv2DPerChannel_Int8_GpuAcc_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc};
- Conv2DPerChannelInt8Test(backends);
-}
-
-} //End of TEST_SUITE("Convolution2dTest_GpuAcc")
-
-void TransposeConvInt8Test(std::vector<armnn::BackendId>& backends)
-{
- // Set input data
- std::vector<int32_t> transposeTensorShape { 4 };
- std::vector<int32_t> filterShape { 1, 2, 2, 1 };
- std::vector<int32_t> inputShape { 1, 2, 2, 1 };
- std::vector<int32_t> outputShape { 1, 3, 3, 1 };
-
- std::vector<int32_t> transposeData = { 1, 3, 3, 1 };
- static std::vector<int8_t> inputValues = { 1, 2, 3, 4 };
- std::vector<int8_t> filterValues = { 0, 1, 2, 4 };
- std::vector<int8_t> expectedOutputValues =
- {
- 0, 1, 2,
- 2, 11, 12,
- 6, 20, 16
- };
-
- tflite::Padding padding = tflite::Padding_VALID;
- TransposeConvTest<int8_t>(backends,
- ::tflite::TensorType_INT8,
- 1, // strideX
- 1, // strideY
- padding,
- transposeTensorShape,
- filterShape,
- inputShape,
- outputShape,
- transposeData,
- filterValues,
- inputValues,
- expectedOutputValues);
-}
-
-void TransposeConvFp32Test(std::vector<armnn::BackendId>& backends)
-{
- std::vector<int32_t> transposeTensorShape { 4 };
- std::vector<int32_t> filterShape { 1, 2, 2, 1 };
- std::vector<int32_t> inputShape { 1, 2, 2, 1 };
- std::vector<int32_t> outputShape { 1, 3, 3, 1 };
-
- std::vector<int32_t> transposeData = { 1, 3, 3, 1 };
- static std::vector<float> inputValues = { 1, 2, 3, 4 };
- std::vector<float> filterValues = { 0, 1, 2, 4 };
- std::vector<float> expectedOutputValues =
- {
- 0, 1, 2,
- 2, 11, 12,
- 6, 20, 16
- };
-
- tflite::Padding padding = tflite::Padding_VALID;
- TransposeConvTest<float>(backends,
- ::tflite::TensorType_FLOAT32,
- 1, // strideX
- 1, // strideY
- padding,
- transposeTensorShape,
- filterShape,
- inputShape,
- outputShape,
- transposeData,
- filterValues,
- inputValues,
- expectedOutputValues);
-}
-
-TEST_SUITE("TransposeConv_CpuRef_Test")
-{
-
-TEST_CASE ("TransposeConv_CpuRef_Fp32_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef};
- TransposeConvFp32Test(backends);
-}
-
-TEST_CASE ("TransposeConv_CpuRef_Int8_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef};
- TransposeConvInt8Test(backends);
-}
-
-} // End of TEST_SUITE(TransposeConv_CpuRef_Test)
-
-TEST_SUITE("TransposeConv_CpuAcc_Test")
-{
-
-TEST_CASE ("TransposeConv_CpuAcc_Fp32_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc};
- TransposeConvFp32Test(backends);
-}
-
-TEST_CASE ("TransposeConv_CpuAcc_Int8_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc};
- TransposeConvInt8Test(backends);
-}
-
-} // End of TEST_SUITE(TransposeConv_CpuAcc_Test)
-
-TEST_SUITE("TransposeConv_GpuAcc_Test")
-{
-
-TEST_CASE ("TransposeConv_GpuAcc_Fp32_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc};
- TransposeConvFp32Test(backends);
-}
-
-TEST_CASE ("TransposeConv_GpuAcc_Int8_Test")
-{
- std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc};
- TransposeConvInt8Test(backends);
-}
-
-} // End of TEST_SUITE(TransposeConv_GpuAcc_Test)
-
-} // namespace armnnDelegate \ No newline at end of file
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-25 18:18:28 +0000