| /* |
| * Copyright (c) 2017-2021, 2023-2024 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "arm_compute/runtime/TensorAllocator.h" |
| #include "src/core/helpers/MemoryHelpers.h" |
| #include "src/cpu/operators/CpuFullyConnected.h" |
| #include "tests/NEON/Accessor.h" |
| #include "tests/PaddingCalculator.h" |
| #include "tests/datasets/FullyConnectedLayerDataset.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/framework/datasets/Datasets.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/fixtures/FullyConnectedLayerFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| using framework::dataset::make; |
| namespace |
| { |
| /** Tolerance for float operations */ |
| constexpr RelativeTolerance<float> rel_tolerance_f32(0.01f); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType::F32 */ |
| constexpr AbsoluteTolerance<float> abs_tolerance_f32(0.001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for DataType::F32 */ |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| const AbsoluteTolerance<float> abs_tolerance_f16(0.3f); /**< Absolute tolerance value for comparing reference's output against implementation's output for DataType::F16 */ |
| const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType::F16 */ |
| constexpr float tolerance_num_f16 = 0.07f; /**< Tolerance number for FP16 */ |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC*/ |
| |
| /** Tolerance for quantized asymmetric operations */ |
| constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1); |
| constexpr AbsoluteTolerance<int8_t> tolerance_qasymm8_signed(1); |
| |
| /** CNN data types */ |
| const auto CNNDataTypes = make("DataType", |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| DataType::F16, |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| DataType::F32, |
| }); |
| |
| const auto FullyConnectedParameters = combine(make("TransposeWeights", { false, true }), make("ReshapeWeights", { false, true })); |
| |
| const auto QuantizationData = make("QuantizationInfo", |
| { |
| QuantizationInfo(1.f / 256.f, 10), |
| QuantizationInfo(1.1f, 10), |
| }); |
| |
| const auto IgnoredQuantizationData = make("IgnoredQuantizationInfo", |
| { |
| QuantizationInfo(), |
| }); |
| |
| const auto NoActivationFunctionDataset = make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| }); |
| |
| const auto ActivationFunctionsDataset = make("ActivationInfo", |
| { |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.75f, 0.25f), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH), |
| }); |
| |
| const auto ActivationFunctionsQuantizedDataset = make("ActivationInfo", |
| { |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.75f, 0.25f), |
| }); |
| } // namespace |
| |
| TEST_SUITE(NEON) |
| TEST_SUITE(FullyConnectedLayer) |
| |
| /** Test case for memory injection in @ref cpu::CpuFullyConnected. |
| * |
| * Configure the operator once and inject memory at run-time in multiple executions. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) |
| { |
| auto fc = std::make_unique<cpu::CpuFullyConnected>(); |
| const auto src_info = TensorInfo(TensorShape(8U), 1, DataType::F32, DataLayout::NHWC); |
| const auto weight_info = TensorInfo(TensorShape(8U, 4U), 1, DataType::F32, DataLayout::NHWC); |
| const auto bias_info = TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC); |
| auto dst_info = TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC); |
| const auto fc_info = FullyConnectedLayerInfo{}; |
| fc->configure(&src_info, &weight_info, &bias_info, &dst_info, fc_info); |
| |
| // telhs are newly created every call of this lambda function |
| auto src = create_tensor<Tensor>(src_info); |
| auto weight = create_tensor<Tensor>(weight_info); |
| auto bias = create_tensor<Tensor>(bias_info); |
| src.allocator()->allocate(); |
| weight.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| |
| ITensorPack run_pack{ { TensorType::ACL_SRC_0, &src }, { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; |
| ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; |
| |
| auto mg = MemoryGroup{}; |
| auto ws = manage_workspace<Tensor>(fc->workspace(), mg, run_pack, prep_pack); |
| |
| auto run_conv = [&]() -> Tensor |
| { |
| auto dst = create_tensor<Tensor>(dst_info); |
| dst.allocator()->allocate(); |
| run_pack.add_tensor(TensorType::ACL_DST, &dst); |
| |
| library->fill_tensor_value(Accessor(src), 1.f); |
| library->fill_tensor_value(Accessor(weight), 2.f); |
| library->fill_tensor_value(Accessor(bias), 3.f); |
| // This operator is configured once and captured by this lambda. |
| fc->prepare(prep_pack); |
| fc->run(run_pack); |
| return dst; |
| }; |
| auto result_0 = run_conv(); |
| auto result_1 = run_conv(); |
| for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| /** Test case for memory injection in @ref NEFullyConnectedLayer. |
| * |
| * Make sure @ref NEFullyConnectedLayer still works through injecting the memory at configure time using the old API. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) |
| { |
| auto fc = std::make_unique<NEFullyConnectedLayer>(); |
| const auto src_info = TensorInfo(TensorShape(8U), 1, DataType::F32, DataLayout::NHWC); |
| const auto weight_info = TensorInfo(TensorShape(8U, 4U), 1, DataType::F32, DataLayout::NHWC); |
| const auto bias_info = TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC); |
| auto dst_info = TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC); |
| const auto fc_info = FullyConnectedLayerInfo{}; |
| auto run_conv = [&]() |
| { |
| auto src = create_tensor<Tensor>(src_info); |
| auto weight = create_tensor<Tensor>(weight_info); |
| auto bias = create_tensor<Tensor>(bias_info); |
| auto dst = create_tensor<Tensor>(dst_info); |
| fc->configure(&src, &weight, &bias, &dst, fc_info); |
| src.allocator()->allocate(); |
| weight.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| library->fill_tensor_value(Accessor(src), 1.f); |
| library->fill_tensor_value(Accessor(weight), 2.f); |
| library->fill_tensor_value(Accessor(bias), 3.f); |
| fc->run(); |
| return dst; |
| }; |
| auto result_0 = run_conv(); |
| auto result_1 = run_conv(); |
| for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| /** Unit test for @ref cpu::CpuFullyConnected with quantized multipler > 1 |
| * |
| * Tests output correctness. |
| */ |
| TEST_CASE(Quant8_Signed_Mult_gt_1, framework::DatasetMode::ALL) |
| { |
| auto fc = std::make_unique<cpu::CpuFullyConnected>(); |
| const auto src_info = TensorInfo(TensorShape(1U, 3U), 1, DataType::QASYMM8_SIGNED, QuantizationInfo(0.5f, -1)); |
| const auto weight_info = TensorInfo(TensorShape(1U), 1, DataType::QASYMM8_SIGNED, QuantizationInfo(0.5, -8)); |
| const auto bias_info = TensorInfo(TensorShape(1U), 1, DataType::S32); |
| auto dst_info = TensorInfo(TensorShape(1U, 3U), 1, DataType::QASYMM8_SIGNED, QuantizationInfo(0.1f, 0)); |
| const auto fc_info = FullyConnectedLayerInfo{}; |
| fc->configure(&src_info, &weight_info, &bias_info, &dst_info, fc_info); |
| |
| // telhs are newly created every call of this lambda function |
| auto src = create_tensor<Tensor>(src_info); |
| auto weight = create_tensor<Tensor>(weight_info); |
| auto bias = create_tensor<Tensor>(bias_info); |
| auto dst = create_tensor<Tensor>(dst_info); |
| src.allocator()->allocate(); |
| weight.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ITensorPack run_pack{ { TensorType::ACL_SRC_0, &src }, { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias }, { TensorType::ACL_DST, &dst } }; |
| ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; |
| |
| auto mg = MemoryGroup{}; |
| auto ws = manage_workspace<Tensor>(fc->workspace(), mg, run_pack, prep_pack); |
| |
| // Initialize input values |
| const std::vector<int8_t> src_values = { 3, 63, 31 }; |
| const std::vector<int8_t> weight_values = { -4 }; |
| const std::vector<int32_t> bias_values = { 16 }; |
| const std::vector<int32_t> expected = { 80, 127, 127 }; |
| library->fill_static_values(Accessor(src), src_values); |
| library->fill_static_values(Accessor(weight), weight_values); |
| library->fill_static_values(Accessor(bias), bias_values); |
| |
| // Run FC layer |
| fc->prepare(prep_pack); |
| fc->run(run_pack); |
| |
| auto dst_ptr = reinterpret_cast<int8_t *>(dst.buffer()); |
| for(size_t i = 0; i < dst.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(dst_ptr[i] == expected[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| // *INDENT-OFF* |
| // clang-format off |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( |
| make("InputInfo", { TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Mismatching data types |
| TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Invalid weights dimensions |
| TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), // Wrongly reshaped weights |
| TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), |
| }), |
| make("WeightsInfo",{ TensorInfo(TensorShape(315U, 271U), 1, DataType::F16), |
| TensorInfo(TensorShape(192U, 192U), 1, DataType::F32), |
| TensorInfo(TensorShape(192U, 192U), 1, DataType::F32), |
| TensorInfo(TensorShape(217U, 315U), 1, DataType::F32), |
| TensorInfo(TensorShape(217U, 315U), 1, DataType::F32), |
| TensorInfo(TensorShape(192U, 192U), 1, DataType::F32), |
| })), |
| make("BiasInfo",{ TensorInfo(TensorShape(271U), 1, DataType::F32), |
| TensorInfo(TensorShape(192U), 1, DataType::F32), |
| TensorInfo(TensorShape(192U), 1, DataType::F32), |
| TensorInfo(TensorShape(271U), 1, DataType::F32), |
| TensorInfo(TensorShape(271U), 1, DataType::F32), |
| TensorInfo(TensorShape(192U), 1, DataType::F32), |
| })), |
| make("OutputInfo",{ TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), |
| TensorInfo(TensorShape(192U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(192U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), |
| TensorInfo(TensorShape(271U, 3U), 1, DataType::F32), |
| TensorInfo(TensorShape(192U, 4U), 1, DataType::F32), |
| })), |
| make("TransposeWeights",{ true, true, false, true, true, true })), |
| make("ReshapedWeights",{ false, false, false, false, false , false})), |
| make("Expected", { false, true, true, false, false, true })), |
| input_info, weights_info, bias_info, output_info, transpose_weights, reshaped_weights, expected) |
| { |
| // Create Fully Connected layer info |
| FullyConnectedLayerInfo fc_info; |
| fc_info.transpose_weights = transpose_weights; |
| fc_info.are_weights_reshaped = reshaped_weights; |
| |
| Status status = NEFullyConnectedLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), fc_info); |
| ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); |
| } |
| // clang-format on |
| // *INDENT-ON* |
| |
| template <typename T> |
| using NEFullyConnectedLayerFixture = FullyConnectedLayerValidationFixture<Tensor, Accessor, NEFullyConnectedLayer, T>; |
| template <typename T> |
| using NEFullyConnectedLayerMixedDataLayoutFixture = FullyConnectedLayerValidationFixture<Tensor, Accessor, NEFullyConnectedLayer, T, true>; |
| template <typename T> |
| using NEFullyConnectedLayerDynamicWeightsFixture = FullyConnectedWithDynamicWeightsFixture<Tensor, Accessor, NEFullyConnectedLayer, T>; |
| template <typename T> |
| using NEFullyConnectedLayerDynamicBiasFixture = FullyConnectedWithDynamicBiasFixture<Tensor, Accessor, NEFullyConnectedLayer, T>; |
| |
| TEST_SUITE(Float) |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| TEST_SUITE(FP16) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| FullyConnectedParameters, |
| make("DataType", DataType::F16), |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16, abs_tolerance_f16); |
| } |
| FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::PRECOMMIT, |
| combine(datasets::FullyConnectedLayerWithActivationDataset(), |
| FullyConnectedParameters, |
| make("DataType", DataType::F16), |
| ActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16, abs_tolerance_f16); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeFullyConnectedLayerDataset(), |
| FullyConnectedParameters, |
| make("DataType", DataType::F16), |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16, abs_tolerance_f16); |
| } |
| FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<half>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| make("DataType", DataType::F16), |
| make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), |
| make("WeightsReshaped", { false, true }))) |
| { |
| } |
| TEST_SUITE_END() |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| TEST_SUITE(FP32) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters, |
| make("DataType", DataType::F32), |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine( |
| make("Input", TensorShape(9U, 5U, 7U)), |
| make("Weights", TensorShape(315U, 271U)), |
| make("Biases", TensorShape(271U)), |
| make("Output", TensorShape(271U)), |
| FullyConnectedParameters, |
| make("DataType", DataType::F32), |
| make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::FullyConnectedLayerWithActivationDataset(), |
| FullyConnectedParameters, |
| make("DataType", DataType::F32), |
| ActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters, |
| make("DataType", DataType::F32), |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f32, 0, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| make("DataType", DataType::F32), |
| make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), |
| make("WeightsReshaped", { false, true }))) |
| { |
| } |
| TEST_SUITE_END() |
| TEST_SUITE_END() |
| |
| template <typename T> |
| using NEFullyConnectedLayerQuantizedFixture = FullyConnectedLayerValidationQuantizedFixture<Tensor, Accessor, NEFullyConnectedLayer, T>; |
| template <typename T> |
| using NEFullyConnectedLayerQuantizedMixedDataLayoutFixture = FullyConnectedLayerValidationQuantizedFixture<Tensor, Accessor, NEFullyConnectedLayer, T, true>; |
| |
| TEST_SUITE(Quantized) |
| TEST_SUITE(QASYMM8) |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayoutWithActivation, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, |
| combine( |
| make("Input", TensorShape(9U, 5U, 7U)), |
| make("Weights", TensorShape(315U, 271U)), |
| make("Biases", TensorShape(271U)), |
| make("Output", TensorShape(271U)), |
| FullyConnectedParameters, |
| make("DataType", DataType::QASYMM8), |
| QuantizationData, |
| make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunSmallWithActivation, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, |
| combine(datasets::FullyConnectedLayerWithActivationDataset(), |
| FullyConnectedParameters, |
| make("DataType", DataType::QASYMM8), |
| QuantizationData, |
| ActivationFunctionsQuantizedDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunDynamicWeightsWithActivation, NEFullyConnectedLayerDynamicWeightsFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| make("DataType", DataType::QASYMM8), |
| make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), |
| make("WeightsReshaped", { false }))) |
| { |
| } |
| FIXTURE_DATA_TEST_CASE(RunDynamicBiasWithActivation, NEFullyConnectedLayerDynamicBiasFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| make("DataType", DataType::QASYMM8), |
| make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) |
| { |
| } |
| |
| // Dynamic Quantization Tests here |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, |
| combine(datasets::SmallFullyConnectedLayerDataset(), |
| FullyConnectedParameters, |
| make("DataType", DataType::QASYMM8), |
| IgnoredQuantizationData, |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine( |
| datasets::LargeFullyConnectedLayerDataset(), |
| FullyConnectedParameters, |
| framework::dataset::make("DataType", DataType::QASYMM8), |
| QuantizationData, |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunDynamicBias, NEFullyConnectedLayerDynamicBiasFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| make("DataType", DataType::QASYMM8), |
| NoActivationFunctionDataset)) |
| { |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, |
| combine( |
| make("Input", TensorShape(9U, 5U, 7U)), |
| make("Weights", TensorShape(315U, 271U)), |
| make("Biases", TensorShape(271U)), |
| make("Output", TensorShape(271U)), |
| FullyConnectedParameters, |
| make("DataType", DataType::QASYMM8), |
| IgnoredQuantizationData, |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| make("DataType", DataType::QASYMM8), |
| NoActivationFunctionDataset, |
| make("WeightsReshaped", { false }))) |
| { |
| } |
| TEST_SUITE_END() // QASYMM8 |
| TEST_SUITE(QASYMM8_SIGNED) |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayoutWithActivation, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, |
| combine( |
| make("Input", TensorShape(9U, 5U, 7U)), |
| make("Weights", TensorShape(315U, 271U)), |
| make("Biases", TensorShape(271U)), |
| make("Output", TensorShape(271U)), |
| FullyConnectedParameters, |
| make("DataType", DataType::QASYMM8_SIGNED), |
| QuantizationData, |
| make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8_signed); |
| } |
| FIXTURE_DATA_TEST_CASE(RunWithActivation, NEFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, |
| combine(datasets::FullyConnectedLayerWithActivationDataset(), |
| FullyConnectedParameters, |
| make("DataType", DataType::QASYMM8_SIGNED), |
| QuantizationData, |
| ActivationFunctionsQuantizedDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8_signed); |
| } |
| FIXTURE_DATA_TEST_CASE(RunDynamicWeightsWithActivation, NEFullyConnectedLayerDynamicWeightsFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| make("DataType", DataType::QASYMM8_SIGNED), |
| make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)), |
| make("WeightsReshaped", { false }))) |
| { |
| } |
| |
| // Dynamic Quantization tests |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine( |
| datasets::SmallFullyConnectedLayerDataset(), |
| FullyConnectedParameters, |
| make("DataType", DataType::QASYMM8_SIGNED), |
| IgnoredQuantizationData, |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8_signed); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEFullyConnectedLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, |
| combine( |
| make("Input", TensorShape(9U, 5U, 7U)), |
| make("Weights", TensorShape(315U, 271U)), |
| make("Biases", TensorShape(271U)), |
| make("Output", TensorShape(271U)), |
| FullyConnectedParameters, |
| make("DataType", DataType::QASYMM8_SIGNED), |
| QuantizationData, |
| NoActivationFunctionDataset)) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8_signed); |
| } |
| FIXTURE_DATA_TEST_CASE(RunDynamicWeights, NEFullyConnectedLayerDynamicWeightsFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallFullyConnectedLayerDataset(), |
| make("DataType", DataType::QASYMM8_SIGNED), |
| NoActivationFunctionDataset, |
| make("WeightsReshaped", { false }))) |
| { |
| } |
| TEST_SUITE_END() // QASYMM8_SIGNED |
| TEST_SUITE_END() // Quantized |
| TEST_SUITE_END() // FullyConnectedLayer |
| TEST_SUITE_END() // NEON |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |