| // |
| // Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "TestUtils.hpp" |
| |
| namespace armnnDelegate |
| { |
| |
| void CompareData(bool tensor1[], bool tensor2[], size_t tensorSize) |
| { |
| auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));}; |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(compareBool(tensor1[i], tensor2[i])); |
| } |
| } |
| |
| void CompareData(std::vector<bool>& tensor1, std::vector<bool>& tensor2, size_t tensorSize) |
| { |
| auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));}; |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(compareBool(tensor1[i], tensor2[i])); |
| } |
| } |
| |
| void CompareData(float tensor1[], float tensor2[], size_t tensorSize) |
| { |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(tensor1[i] == doctest::Approx( tensor2[i] )); |
| } |
| } |
| |
| void CompareData(float tensor1[], float tensor2[], size_t tensorSize, float percentTolerance) |
| { |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= |
| std::abs(tensor1[i]*percentTolerance/100)); |
| } |
| } |
| |
| void CompareData(uint8_t tensor1[], uint8_t tensor2[], size_t tensorSize) |
| { |
| uint8_t tolerance = 1; |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance); |
| } |
| } |
| |
| void CompareData(int16_t tensor1[], int16_t tensor2[], size_t tensorSize) |
| { |
| int16_t tolerance = 1; |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance); |
| } |
| } |
| |
| void CompareData(int32_t tensor1[], int32_t tensor2[], size_t tensorSize) |
| { |
| int32_t tolerance = 1; |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance); |
| } |
| } |
| |
| void CompareData(int8_t tensor1[], int8_t tensor2[], size_t tensorSize) |
| { |
| int8_t tolerance = 1; |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance); |
| } |
| } |
| |
| void CompareData(Half tensor1[], Half tensor2[], size_t tensorSize) |
| { |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| CHECK(tensor1[i] == doctest::Approx( tensor2[i] )); |
| } |
| } |
| |
| void CompareData(TfLiteFloat16 tensor1[], TfLiteFloat16 tensor2[], size_t tensorSize) |
| { |
| uint16_t tolerance = 1; |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| uint16_t tensor1Data = tensor1[i].data; |
| uint16_t tensor2Data = tensor2[i].data; |
| CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance); |
| } |
| } |
| |
| void CompareData(TfLiteFloat16 tensor1[], Half tensor2[], size_t tensorSize) { |
| uint16_t tolerance = 1; |
| for (size_t i = 0; i < tensorSize; i++) |
| { |
| uint16_t tensor1Data = tensor1[i].data; |
| uint16_t tensor2Data = half_float::detail::float2half<std::round_indeterminate, float>(tensor2[i]); |
| CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance); |
| } |
| } |
| |
| void CompareOutputShape(const std::vector<int32_t>& tfLiteDelegateShape, |
| const std::vector<int32_t>& armnnDelegateShape, |
| const std::vector<int32_t>& expectedOutputShape) |
| { |
| CHECK(expectedOutputShape.size() == tfLiteDelegateShape.size()); |
| CHECK(expectedOutputShape.size() == armnnDelegateShape.size()); |
| |
| for (size_t i = 0; i < expectedOutputShape.size(); i++) |
| { |
| CHECK(expectedOutputShape[i] == armnnDelegateShape[i]); |
| CHECK(tfLiteDelegateShape[i] == expectedOutputShape[i]); |
| CHECK(tfLiteDelegateShape[i] == armnnDelegateShape[i]); |
| } |
| } |
| |
| } // namespace armnnDelegate |