| // |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "FullyConnectedTestHelper.hpp" |
| |
| namespace |
| { |
| |
| TEST_SUITE("FullyConnectedTest") |
| { |
| |
| void FullyConnectedFp32Test(std::vector<armnn::BackendId>& backends) |
| { |
| std::vector<int32_t> inputTensorShape { 1, 4, 1, 1 }; |
| std::vector<int32_t> weightsTensorShape { 1, 4 }; |
| std::vector<int32_t> biasTensorShape { 1 }; |
| std::vector<int32_t> outputTensorShape { 1, 1 }; |
| |
| std::vector<float> inputValues = { 10, 20, 30, 40 }; |
| std::vector<float> weightsData = { 2, 3, 4, 5 }; |
| |
| std::vector<float> expectedOutputValues = { (400 + 10) }; |
| |
| // bias is set std::vector<float> biasData = { 10 } in the model |
| FullyConnectedTest<float>(backends, |
| ::tflite::TensorType_FLOAT32, |
| tflite::ActivationFunctionType_NONE, |
| inputTensorShape, |
| weightsTensorShape, |
| biasTensorShape, |
| outputTensorShape, |
| inputValues, |
| expectedOutputValues, |
| weightsData); |
| } |
| |
| void FullyConnectedActicationTest(std::vector<armnn::BackendId>& backends) |
| { |
| std::vector<int32_t> inputTensorShape { 1, 4, 1, 1 }; |
| std::vector<int32_t> weightsTensorShape { 1, 4 }; |
| std::vector<int32_t> biasTensorShape { 1 }; |
| std::vector<int32_t> outputTensorShape { 1, 1 }; |
| |
| std::vector<float> inputValues = { -10, 20, 30, 40 }; |
| std::vector<float> weightsData = { 2, 3, 4, -5 }; |
| |
| std::vector<float> expectedOutputValues = { 0 }; |
| |
| // bias is set std::vector<float> biasData = { 10 } in the model |
| FullyConnectedTest<float>(backends, |
| ::tflite::TensorType_FLOAT32, |
| tflite::ActivationFunctionType_RELU, |
| inputTensorShape, |
| weightsTensorShape, |
| biasTensorShape, |
| outputTensorShape, |
| inputValues, |
| expectedOutputValues, |
| weightsData); |
| } |
| |
| void FullyConnectedUint8Test(std::vector<armnn::BackendId>& backends) |
| { |
| std::vector<int32_t> inputTensorShape { 1, 4, 2, 1 }; |
| std::vector<int32_t> weightsTensorShape { 1, 4 }; |
| std::vector<int32_t> biasTensorShape { 1 }; |
| std::vector<int32_t> outputTensorShape { 2, 1 }; |
| |
| std::vector<uint8_t> inputValues = { 1, 2, 3, 4, 10, 20, 30, 40 }; |
| std::vector<uint8_t> weightsData = { 2, 3, 4, 5 }; |
| |
| std::vector<uint8_t> expectedOutputValues = { (40 + 10) / 2, (400 + 10) / 2 }; |
| |
| // bias is set std::vector<int32_t> biasData = { 10 } in the model |
| // input and weights quantization scale 1.0f and offset 0 in the model |
| // output quantization scale 2.0f and offset 0 in the model |
| FullyConnectedTest<uint8_t>(backends, |
| ::tflite::TensorType_UINT8, |
| tflite::ActivationFunctionType_NONE, |
| inputTensorShape, |
| weightsTensorShape, |
| biasTensorShape, |
| outputTensorShape, |
| inputValues, |
| expectedOutputValues, |
| weightsData); |
| } |
| |
| TEST_CASE ("FULLY_CONNECTED_FP32_GpuAcc_Test") |
| { |
| std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, |
| armnn::Compute::CpuRef }; |
| FullyConnectedFp32Test(backends); |
| } |
| |
| TEST_CASE ("FULLY_CONNECTED_FP32_CpuAcc_Test") |
| { |
| std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, |
| armnn::Compute::CpuRef }; |
| FullyConnectedFp32Test(backends); |
| } |
| |
| TEST_CASE ("FULLY_CONNECTED_UINT8_GpuAcc_Test") |
| { |
| std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, |
| armnn::Compute::CpuRef }; |
| FullyConnectedUint8Test(backends); |
| } |
| |
| TEST_CASE ("FULLY_CONNECTED_UINT8_CpuAcc_Test") |
| { |
| std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, |
| armnn::Compute::CpuRef }; |
| FullyConnectedUint8Test(backends); |
| } |
| |
| TEST_CASE ("FULLY_CONNECTED_Activation_GpuAcc_Test") |
| { |
| std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, |
| armnn::Compute::CpuRef }; |
| FullyConnectedActicationTest(backends); |
| } |
| |
| } // End of TEST_SUITE("FullyConnectedTest") |
| |
| } // anonymous namespace |