/* * Copyright (c) 2017-2019 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/NEGEMMLowpAssemblyMatrixMultiplyCore.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" #include "tests/NEON/Accessor.h" #include "tests/NEON/Helper.h" #include "tests/PaddingCalculator.h" #include "tests/datasets/GEMMLowpFusedOffsetOutputDataset.h" #include "tests/datasets/LargeGEMMLowpDataset.h" #include "tests/datasets/ShapeDatasets.h" #include "tests/datasets/SmallGEMMLowpDataset.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/GEMMLowpAssemblyFixture.h" #include "tests/validation/fixtures/GEMMLowpFixture.h" namespace arm_compute { namespace test { namespace validation { namespace { const auto data_matrix_multiply = framework::dataset::make("M", 12, 20) * framework::dataset::make("N", 12, 20) * framework::dataset::make("K", 16); } // namespace TEST_SUITE(NEON) TEST_SUITE(ASSEMBLY_MATRIX_MULTIPLY) using NEGEMMAssemblyFixture_S8 = GEMMLowpAssemblyFixture; using NEGEMMAssemblyFixture_U8 = GEMMLowpAssemblyFixture; TEST_SUITE(S8) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMAssemblyFixture_S8, framework::DatasetMode::PRECOMMIT, data_matrix_multiply) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE_END() TEST_SUITE(U8) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMAssemblyFixture_U8, framework::DatasetMode::PRECOMMIT, data_matrix_multiply) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE_END() TEST_SUITE_END() TEST_SUITE(GEMMLowp) TEST_SUITE(MatrixMultiplyCore) using NEGEMMLowpMatrixMultiplyCoreFixture = GEMMLowpMatrixMultiplyCoreValidationFixture; DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallGEMMLowpDataset(), datasets::LargeGEMMLowpDataset()), shape_a, shape_b, shape_c, a_offset, b_offset) { // Create tensors Tensor a = create_tensor(shape_a, DataType::QASYMM8); Tensor b = create_tensor(shape_b, DataType::QASYMM8); Tensor c = create_tensor(shape_c, DataType::S32); a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset)); b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset)); ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function NEGEMMLowpMatrixMultiplyCore gemmlowp_mm; gemmlowp_mm.configure(&a, &b, nullptr, &c); } // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Input not a multiple of 4 TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Mismatching data type TensorInfo(TensorShape(20U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions TensorInfo(TensorShape(16U, 32U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), }), framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)), TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)), TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)), TensorInfo(TensorShape(33U, 21U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)), TensorInfo(TensorShape(64U, 16U), 1, DataType::QASYMM8, QuantizationInfo(1.f/256, 10)), })), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(33U, 13U), 1, DataType::S32), TensorInfo(TensorShape(33U, 13U), 1, DataType::S32), TensorInfo(TensorShape(33U, 13U), 1, DataType::S32), TensorInfo(TensorShape(8U, 11U), 1, DataType::S32), TensorInfo(TensorShape(64U, 32U), 1, DataType::S32), })), framework::dataset::make("Expected", { false, false, false, false, true })), a_info, b_info, output_info, expected) { // Lock tensors Status status = NEGEMMLowpMatrixMultiplyCore::validate(&a_info.clone()->set_is_resizable(false), &b_info.clone()->set_is_resizable(false), nullptr, &output_info.clone()->set_is_resizable(false)); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFixture, framework::DatasetMode::ALL, datasets::SmallGEMMLowpDataset()) { // Validate output validate(Accessor(_target), _reference); } FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpMatrixMultiplyCoreFixture, framework::DatasetMode::NIGHTLY, datasets::LargeGEMMLowpDataset()) { // Validate output validate(Accessor(_target), _reference); } using NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture = GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture; TEST_SUITE(FusedOffsetOutput) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::ALL, datasets::SmallGEMMLowpFusedOffsetOutputDataset()) { // Validate output validate(Accessor(_target), _reference); } FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpMatrixMultiplyCoreFusedOffsetOutputFixture, framework::DatasetMode::NIGHTLY, datasets::LargeGEMMLowpFusedOffsetOutputDataset()) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE_END() // FusedOffsetOutput TEST_SUITE_END() // MatrixMultiplyCore TEST_SUITE(OutputStage) TEST_SUITE(QuantizeDownInt32ToUint8Scale) const auto quantize_down_int32_to_uint8_scale_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true }); const auto quantize_down_int32_to_uint8_scale_relu_cases = framework::dataset::make("result_offset", -2, 1) * framework::dataset::make("result_mult_int", 1, 2) * framework::dataset::make("result_shift", 2, 3) * framework::dataset::make("min", 0, 2) * framework::dataset::make("max", 171, 174) * framework::dataset::make("addBias", { false, true }); using NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture; // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16 TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type }), framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32), TensorInfo(TensorShape(21U), 1, DataType::S32), TensorInfo(TensorShape(20U), 1, DataType::S32), })), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8), TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8), TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), })), framework::dataset::make("Min",{ 0, 8, 13, })), framework::dataset::make("Max",{ 205, 300, 180, })), framework::dataset::make("Expected", { true, false, false })), a_info, b_info, output_info, min, max, expected) { // Lock tensors Status status = NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(&a_info.clone()->set_is_resizable(false), &b_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), min, max); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases), shape, result_offset, result_mult_int, result_shift, min, max, add_bias) { TensorShape shape_bias(shape[0]); // Create tensors Tensor in = create_tensor(shape, DataType::S32); Tensor bias = create_tensor(shape_bias, DataType::S32); Tensor out = create_tensor(shape, DataType::QASYMM8); ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function NEGEMMLowpQuantizeDownInt32ToUint8Scale output_stage; output_stage.configure(&in, add_bias ? &bias : nullptr, &out, result_offset, result_mult_int, result_shift, min, max); // Validate valid region input and output const ValidRegion valid_region = shape_to_valid_region(shape); validate(in.info()->valid_region(), valid_region); validate(out.info()->valid_region(), valid_region); // Validate valid region bias if(add_bias) { const ValidRegion valid_region_bias = shape_to_valid_region(shape_bias); validate(bias.info()->valid_region(), valid_region_bias); } // Validate padding const PaddingSize padding(0); validate(in.info()->padding(), padding); validate(out.info()->padding(), padding); if(add_bias) { validate(bias.info()->padding(), padding); } } FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_cases)) { // Validate output validate(Accessor(_target), _reference); } FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_cases)) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE(BoundedReLu) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_relu_cases)) { // Validate output validate(Accessor(_target), _reference); } FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_relu_cases)) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE_END() // BoundedReLu TEST_SUITE_END() // QuantizeDownInt32ToUint8Scale TEST_SUITE(QuantizeDownInt32ToUint8ScaleByFixedPoint) const auto quantize_down_int32_to_uint8_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1, 2) * framework::dataset::make("result_offset_after_shift", 2, 3) * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true }); const auto quantize_down_int32_to_uint8_scale_by_fixedpoint_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1, 2) * framework::dataset::make("result_offset_after_shift", 2, 3) * framework::dataset::make("min", 0, 2) * framework::dataset::make("max", 171, 174) * framework::dataset::make("addBias", { false, true }); using NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture = GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture; using NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture = GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture; // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16 TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type }), framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32), TensorInfo(TensorShape(21U), 1, DataType::S32), TensorInfo(TensorShape(20U), 1, DataType::S32), })), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8), TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8), TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), })), framework::dataset::make("Min",{ 0, 8, 13, })), framework::dataset::make("Max",{ 205, 300, 180, })), framework::dataset::make("Expected", { true, false, false })), a_info, b_info, output_info, min, max, expected) { // Lock tensors Status status = NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(false), &b_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), min, max); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_by_fixedpoint_cases), shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias) { TensorShape shape_bias(shape[0]); // Create tensors Tensor in = create_tensor(shape, DataType::S32); Tensor bias = create_tensor(shape_bias, DataType::S32); Tensor out = create_tensor(shape, DataType::QASYMM8); ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint output_stage; output_stage.configure(&in, add_bias ? &bias : nullptr, &out, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); // Validate valid region input and output const ValidRegion valid_region = shape_to_valid_region(shape); validate(in.info()->valid_region(), valid_region); validate(out.info()->valid_region(), valid_region); // Validate valid region bias if(add_bias) { const ValidRegion valid_region_bias = shape_to_valid_region(shape_bias); validate(bias.info()->valid_region(), valid_region_bias); } // Validate padding const PaddingSize padding(0); validate(in.info()->padding(), padding); validate(out.info()->padding(), padding); if(add_bias) { validate(bias.info()->padding(), padding); } } FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_by_fixedpoint_cases)) { // Validate output validate(Accessor(_target), _reference); } FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_by_fixedpoint_cases)) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE(BoundedReLu) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_uint8_scale_by_fixedpoint_relu_cases)) { // Validate output validate(Accessor(_target), _reference); } FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), quantize_down_int32_to_uint8_scale_by_fixedpoint_relu_cases)) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE_END() // BoundedReLu TEST_SUITE_END() // QuantizeDownInt32ToUint8ScaleByFixedPoint TEST_SUITE(QuantizeDownInt32ToInt16ScaleByFixedPoint) const auto quantize_down_int32_to_int16_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1, 2) * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true }); const auto quantize_down_int32_to_int16_scale_by_fixedpoint_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1, 2) * framework::dataset::make("min", -2, 0) * framework::dataset::make("max", 1, 3) * framework::dataset::make("addBias", { false, true }); using NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture = GEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointValidationFixture; // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Input not a multiple of 16 TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type }), framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32), TensorInfo(TensorShape(21U), 1, DataType::S32), TensorInfo(TensorShape(20U), 1, DataType::S32), })), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QSYMM16), TensorInfo(TensorShape(21U, 13U), 1, DataType::QSYMM16), TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), })), framework::dataset::make("Min",{ -205, -60000, -180, })), framework::dataset::make("Max",{ 205, 60000, 180, })), framework::dataset::make("Expected", { true, false, false })), a_info, b_info, output_info, min, max, expected) { // Lock tensors Status status = NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(false), &b_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), min, max); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int16_scale_by_fixedpoint_cases), shape, result_fixedpoint_multiplier, result_shift, min, max, add_bias) { TensorShape shape_bias(shape[0]); // Create tensors Tensor in = create_tensor(shape, DataType::S32); Tensor bias = create_tensor(shape_bias, DataType::S32); Tensor out = create_tensor(shape, DataType::QSYMM16); ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint output_stage; output_stage.configure(&in, add_bias ? &bias : nullptr, &out, result_fixedpoint_multiplier, result_shift, min, max); // Validate valid region input and output const ValidRegion valid_region = shape_to_valid_region(shape); validate(in.info()->valid_region(), valid_region); validate(out.info()->valid_region(), valid_region); // Validate valid region bias if(add_bias) { const ValidRegion valid_region_bias = shape_to_valid_region(shape_bias); validate(bias.info()->valid_region(), valid_region_bias); } // Validate padding const PaddingSize padding(0); validate(in.info()->padding(), padding); validate(out.info()->padding(), padding); if(add_bias) { validate(bias.info()->padding(), padding); } } FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int16_scale_by_fixedpoint_cases)) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE(BoundedReLu) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), quantize_down_int32_to_int16_scale_by_fixedpoint_relu_cases)) { // Validate output validate(Accessor(_target), _reference); } TEST_SUITE_END() // BoundedReLu TEST_SUITE_END() // QuantizeDownInt32ToInt16ScaleByFixedPoint TEST_SUITE_END() // OutputStage TEST_SUITE_END() // GEMMLowp TEST_SUITE_END() // NEON } // namespace validation } // namespace test } // namespace arm_compute