| /* | |
| * Copyright (c) 2017-2023 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/CL/CLTensor.h" | |
| #include "arm_compute/runtime/CL/CLTensorAllocator.h" | |
| #include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" | |
| #include "tests/CL/CLAccessor.h" | |
| #include "tests/PaddingCalculator.h" | |
| #include "tests/datasets/DirectConvolutionLayerDataset.h" | |
| #include "tests/datasets/ShapeDatasets.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/DirectConvolutionLayerFixture.h" | |
| /** Synced with tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp | |
| * Please check there for any differences in the coverage | |
| */ | |
| namespace arm_compute | |
| { | |
| namespace test | |
| { | |
| namespace validation | |
| { | |
| namespace | |
| { | |
| RelativeTolerance<half> tolerance_fp16(half(0.2)); /**< Tolerance for floating point tests */ | |
| RelativeTolerance<float> tolerance_fp32(0.05f); /**< Tolerance for floating point tests */ | |
| constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/ | |
| constexpr float tolerance_num = 0.07f; /**< Tolerance number */ | |
| constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1); /**< Tolerance for quantized tests */ | |
| const auto data_strides = combine(framework::dataset::make("StrideX", 1, 3), framework::dataset::make("StrideY", 1, 3)); | |
| const auto data_strides_small = combine(framework::dataset::make("StrideX", 1), framework::dataset::make("StrideY", 1)); | |
| const auto data_ksize_one = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 1))); | |
| const auto data_ksize_one_small = combine(framework::dataset::make("PadX", 0), combine(framework::dataset::make("PadY", 0), framework::dataset::make("KernelSize", 1))); | |
| const auto data_ksize_three = combine(framework::dataset::make("PadX", 0, 2), combine(framework::dataset::make("PadY", 0, 2), framework::dataset::make("KernelSize", 3))); | |
| const auto data_ksize_five = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 5))); | |
| const auto data_ksize_nine = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 9))); | |
| const auto data_ksize_nine_small = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 9))); | |
| const auto data_all_kernels = concat(concat(data_ksize_one, data_ksize_three), data_ksize_five); | |
| const auto data = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_all_kernels)); | |
| const auto data9x9 = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_ksize_nine)); | |
| const auto data_small = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_one_small)); | |
| const auto data_small9x9 = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_nine_small)); | |
| /** Direct convolution nightly data set. */ | |
| const auto data_nightly = combine(data, framework::dataset::make("NumKernels", { 1, 4 })); | |
| const auto data_nightly_9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 1, 4 })); | |
| const auto data_nightly_usecase = combine(framework::dataset::make("InputShape", { TensorShape{ 3U, 800U, 800U } }), | |
| combine(framework::dataset::make("StrideX", { 1 }), | |
| combine(framework::dataset::make("StrideY", { 1 }), | |
| combine(framework::dataset::make("PadX", { 4 }), | |
| combine(framework::dataset::make("PadY", { 4 }), | |
| combine(framework::dataset::make("KernelSize", 9), | |
| framework::dataset::make("NumKernels", { 16 }))))))); | |
| /** Direct convolution precommit data set. */ | |
| const auto data_precommit = combine(data_small, framework::dataset::make("NumKernels", { 1 })); | |
| const auto data_precommit_9x9 = combine(data_small9x9, framework::dataset::make("NumKernels", { 1 })); | |
| /** Activation function Dataset*/ | |
| const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", | |
| { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) }); | |
| } // namespace | |
| TEST_SUITE(CL) | |
| TEST_SUITE(DirectConvolutionLayer) | |
| /** Check whether the configuration of a Direct Convolution layer with no | |
| * bias leads to a successful execution. | |
| */ | |
| TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT) | |
| { | |
| const auto src_shape = TensorShape(27U, 13U, 2U); | |
| const auto weights_shape = TensorShape(3U, 3U, 2U, 4U); | |
| const auto bias_shape = TensorShape(4U); | |
| const auto dst_shape = TensorShape(25U, 11U, 4U); | |
| constexpr auto dt = DataType::F32; | |
| auto src = create_tensor<CLTensor>(src_shape, dt); | |
| auto weights = create_tensor<CLTensor>(weights_shape, dt); | |
| auto dst = create_tensor<CLTensor>(dst_shape, dt); | |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0); | |
| // Create Direct Convolution function | |
| CLDirectConvolutionLayer conv{}; | |
| conv.configure(&src, &weights, nullptr, &dst, conv_info); | |
| src.allocator()->allocate(); | |
| weights.allocator()->allocate(); | |
| dst.allocator()->allocate(); | |
| library->fill_tensor_value(CLAccessor(src), 1.f); | |
| library->fill_tensor_value(CLAccessor(weights), 1.f); | |
| conv.run(); | |
| // Compute reference to compare | |
| SimpleTensor<float> ref_src{ src_shape, dt }; | |
| SimpleTensor<float> ref_weights{ weights_shape, dt }; | |
| SimpleTensor<float> ref_bias{ bias_shape, dt }; | |
| library->fill_tensor_value(ref_src, 1.f); | |
| library->fill_tensor_value(ref_weights, 1.f); | |
| // No bias | |
| library->fill_tensor_value(ref_bias, 0.f); | |
| auto ref_dst = reference::convolution_layer<float>(ref_src, ref_weights, ref_bias, dst_shape, conv_info); | |
| validate(CLAccessor(dst), ref_dst); | |
| } | |
| /** Check whether the case of rectangle kernels i.e. when width and height of the weight_shape are not equal | |
| * would lead to successful run | |
| */ | |
| TEST_CASE(NonSquareKernel, framework::DatasetMode::PRECOMMIT) | |
| { | |
| auto src_shape = TensorShape(33U, 27U, 3U); | |
| auto weights_shape = TensorShape(5U, 7U, 3U, 4U); // non-square kernel | |
| const auto bias_shape = TensorShape(4U); | |
| auto dst_shape = TensorShape(11U, 12U, 4U); | |
| constexpr auto dt = DataType::F32; | |
| TensorShape src_shape_nhwc(src_shape); | |
| TensorShape weights_shape_nhwc(weights_shape); | |
| TensorShape dst_shape_nhwc(dst_shape); | |
| // Non-square shapes are only allowed for NHWC | |
| permute(src_shape_nhwc, PermutationVector(2U, 0U, 1U)); | |
| permute(weights_shape_nhwc, PermutationVector(2U, 0U, 1U)); | |
| permute(dst_shape_nhwc, PermutationVector(2U, 0U, 1U)); | |
| auto src = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC); | |
| auto weights = create_tensor<CLTensor>(weights_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC); | |
| auto dst = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC); | |
| const auto conv_info = PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR); | |
| // Create direct convolution function | |
| CLDirectConvolutionLayer conv{}; | |
| conv.configure(&src, &weights, nullptr, &dst, conv_info); | |
| src.allocator()->allocate(); | |
| weights.allocator()->allocate(); | |
| dst.allocator()->allocate(); | |
| library->fill_tensor_value(CLAccessor(src), 1.f); | |
| library->fill_tensor_value(CLAccessor(weights), 1.f); | |
| conv.run(); | |
| // Compute reference to compare | |
| SimpleTensor<float> ref_src{ src_shape, dt }; | |
| SimpleTensor<float> ref_weights{ weights_shape, dt }; | |
| SimpleTensor<float> ref_bias{ bias_shape, dt }; | |
| library->fill_tensor_value(ref_src, 1.f); | |
| library->fill_tensor_value(ref_weights, 1.f); | |
| // No bias | |
| library->fill_tensor_value(ref_bias, 0.f); | |
| auto ref_dst = reference::convolution_layer<float>(ref_src, ref_weights, ref_bias, dst_shape, conv_info); | |
| validate(CLAccessor(dst), ref_dst); | |
| } | |
| // *INDENT-OFF* | |
| // clang-format off | |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid: Mismatching data type input/weights | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid: Mismatching input feature maps | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported biases size | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported biases dimensions | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size | |
| TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), | |
| }), | |
| framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16), | |
| TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32), | |
| })), | |
| framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(3U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U, 2U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(4U), 1, DataType::F32), | |
| })), | |
| framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32), | |
| TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32), | |
| })), | |
| framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(1, 1, 0, 0), | |
| })), | |
| framework::dataset::make("ActivationInfo", | |
| { | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) | |
| })), | |
| framework::dataset::make("Expected", { false, false, false, false, false, false, true })), | |
| input_info, weights_info, biases_info, output_info, conv_info, act_info, expected) | |
| { | |
| bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info)); | |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); | |
| } | |
| // clang-format on | |
| // *INDENT-ON* | |
| template <typename T> | |
| using CLDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>; | |
| template <typename T> | |
| using CLDirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T, true>; | |
| template <typename T> | |
| using CLDirectConvolutionValidationWithTensorShapesFixture = DirectConvolutionValidationWithTensorShapesFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>; | |
| template <typename T> | |
| using CLDirectConvolutionLayerQuantizedFixture = DirectConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>; | |
| template <typename T> | |
| using CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture = DirectConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T, true>; | |
| template <typename T> | |
| using CLDirectConvolutionValidationWithTensorShapesQuantizedFixture = DirectConvolutionValidationWithTensorShapesQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>; | |
| TEST_SUITE(NHWC) | |
| // *INDENT-OFF* | |
| // clang-format off | |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputInfo", { | |
| TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Arbitrary weight sizes for NHWC are supported | |
| TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Non-rectangular weights dimensions for NHWC are supported | |
| TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Strides > 2 for any kernel sizes for NHWC are supported | |
| }), | |
| framework::dataset::make("WeightsInfo",{ | |
| TensorInfo(TensorShape(2U, 13U, 13U, 4U), 1, DataType::F32, DataLayout::NHWC), | |
| TensorInfo(TensorShape(2U, 5U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), | |
| TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), | |
| })), | |
| framework::dataset::make("BiasesInfo",{ | |
| TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), | |
| TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), | |
| TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), | |
| })), | |
| framework::dataset::make("OutputInfo",{ | |
| TensorInfo(TensorShape(4U, 15U, 1U), 1, DataType::F32, DataLayout::NHWC), | |
| TensorInfo(TensorShape(4U, 23U, 11U), 1, DataType::F32, DataLayout::NHWC), | |
| TensorInfo(TensorShape(4U, 9U, 4U), 1, DataType::F32, DataLayout::NHWC), | |
| })), | |
| framework::dataset::make("ConvInfo", { | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(3, 3, 0, 0), | |
| })), | |
| framework::dataset::make("ActivationInfo", | |
| { | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| })), | |
| framework::dataset::make("Expected", { true, true, true })), | |
| input_info, weights_info, biases_info, output_info, conv_info, act_info, expected) | |
| { | |
| bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info)); | |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); | |
| } | |
| TEST_SUITE(FP16) | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, | |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), | |
| TensorShape(19U, 5U, 16U, 4U), | |
| TensorShape(13U, 5U, 17U, 2U), | |
| TensorShape(32U, 37U, 13U) } ), | |
| framework::dataset::make("StrideX", { 1, 3, 1, 1 })), | |
| framework::dataset::make("StrideY", { 1, 3, 2, 1 })), | |
| framework::dataset::make("PadX", { 1, 3, 0, 4 })), | |
| framework::dataset::make("PadY", { 1, 3, 0, 4 })), | |
| framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), | |
| framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), | |
| framework::dataset::make("DataType", DataType::F16)), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, | |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), | |
| framework::dataset::make("StrideX", { 1 })), | |
| framework::dataset::make("StrideY", { 1 })), | |
| framework::dataset::make("PadX", { 1 })), | |
| framework::dataset::make("PadY", { 1 })), | |
| framework::dataset::make("KernelSize", { 9 })), | |
| framework::dataset::make("NumKernels", { 3 })), | |
| framework::dataset::make("DataType", DataType::F16)), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num); | |
| } | |
| TEST_SUITE_END() // FP16 | |
| TEST_SUITE(FP32) | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, | |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), | |
| TensorShape(19U, 5U, 16U, 4U), | |
| TensorShape(13U, 5U, 17U, 2U), | |
| TensorShape(32U, 37U, 13U) } ), | |
| framework::dataset::make("StrideX", { 1, 3, 1, 1 })), | |
| framework::dataset::make("StrideY", { 1, 3, 2, 1 })), | |
| framework::dataset::make("PadX", { 1, 3, 0, 4 })), | |
| framework::dataset::make("PadY", { 1, 3, 0, 4 })), | |
| framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), | |
| framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), | |
| framework::dataset::make("DataType", DataType::F32)), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, | |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), | |
| TensorShape(19U, 5U, 16U, 4U), | |
| TensorShape(13U, 5U, 17U, 2U), | |
| TensorShape(32U, 37U, 13U) } ), | |
| framework::dataset::make("StrideX", { 1 })), | |
| framework::dataset::make("StrideY", { 2 })), | |
| framework::dataset::make("PadX", { 1 })), | |
| framework::dataset::make("PadY", { 3 })), | |
| framework::dataset::make("KernelSize", { 3 })), | |
| framework::dataset::make("NumKernels", { 3 })), | |
| framework::dataset::make("DataType", DataType::F32)), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, | |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), | |
| framework::dataset::make("StrideX", { 1 })), | |
| framework::dataset::make("StrideY", { 1 })), | |
| framework::dataset::make("PadX", { 1 })), | |
| framework::dataset::make("PadY", { 1 })), | |
| framework::dataset::make("KernelSize", { 9 })), | |
| framework::dataset::make("NumKernels", { 3 })), | |
| framework::dataset::make("DataType", DataType::F32)), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); | |
| } | |
| TEST_SUITE_END() // FP32 | |
| TEST_SUITE(Quantized) | |
| TEST_SUITE(QASYMM8) | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, | |
| combine(combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), | |
| TensorShape(19U, 5U, 16U, 4U), | |
| TensorShape(13U, 5U, 17U, 2U), | |
| TensorShape(32U, 37U, 13U) } ), | |
| framework::dataset::make("StrideX", { 1, 3, 1, 1 })), | |
| framework::dataset::make("StrideY", { 1, 3, 2, 1 })), | |
| framework::dataset::make("PadX", { 1, 3, 0, 4 })), | |
| framework::dataset::make("PadY", { 1, 3, 0, 4 })), | |
| framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), | |
| framework::dataset::make("NumKernels", { 7, 3, 1, 3 })), | |
| framework::dataset::make("DataType", DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", QuantizationInfo(1.1f / 255, 10))), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, | |
| combine(combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), | |
| TensorShape(19U, 5U, 16U, 4U), | |
| TensorShape(13U, 5U, 17U, 2U), | |
| TensorShape(32U, 37U, 13U) } ), | |
| framework::dataset::make("StrideX", { 1 })), | |
| framework::dataset::make("StrideY", { 2 })), | |
| framework::dataset::make("PadX", { 1 })), | |
| framework::dataset::make("PadY", { 1 })), | |
| framework::dataset::make("KernelSize", { 3 })), | |
| framework::dataset::make("NumKernels", { 3 })), | |
| framework::dataset::make("DataType", DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", QuantizationInfo(1.1f / 255, 10))), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, | |
| combine(combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), | |
| framework::dataset::make("StrideX", { 1 })), | |
| framework::dataset::make("StrideY", { 1 })), | |
| framework::dataset::make("PadX", { 1 })), | |
| framework::dataset::make("PadY", { 1 })), | |
| framework::dataset::make("KernelSize", { 9 })), | |
| framework::dataset::make("NumKernels", { 3 })), | |
| framework::dataset::make("DataType", DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| TEST_SUITE_END() // QASYMM8 | |
| TEST_SUITE(QASYMM8_SIGNED) | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT, | |
| combine(combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), | |
| TensorShape(19U, 5U, 16U, 4U), | |
| TensorShape(13U, 5U, 17U, 2U), | |
| TensorShape(32U, 37U, 13U) } ), | |
| framework::dataset::make("StrideX", { 1, 3, 1, 1 })), | |
| framework::dataset::make("StrideY", { 1, 3, 2, 1 })), | |
| framework::dataset::make("PadX", { 1, 3, 0, 4 })), | |
| framework::dataset::make("PadY", { 1, 3, 0, 4 })), | |
| framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), | |
| framework::dataset::make("NumKernels", { 7, 3, 1, 3 })), | |
| framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), | |
| framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT, | |
| combine(combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), | |
| TensorShape(19U, 5U, 16U, 4U), | |
| TensorShape(13U, 5U, 17U, 2U), | |
| TensorShape(32U, 37U, 13U) } ), | |
| framework::dataset::make("StrideX", { 1 })), | |
| framework::dataset::make("StrideY", { 1 })), | |
| framework::dataset::make("PadX", { 1 })), | |
| framework::dataset::make("PadY", { 1 })), | |
| framework::dataset::make("KernelSize", { 3 })), | |
| framework::dataset::make("NumKernels", { 3 })), | |
| framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), | |
| framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY, | |
| combine(combine(combine(combine(zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), | |
| framework::dataset::make("StrideX", { 1 })), | |
| framework::dataset::make("StrideY", { 1 })), | |
| framework::dataset::make("PadX", { 1 })), | |
| framework::dataset::make("PadY", { 1 })), | |
| framework::dataset::make("KernelSize", { 9 })), | |
| framework::dataset::make("NumKernels", { 3 })), | |
| framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), | |
| framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))), | |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), | |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| TEST_SUITE_END() // QASYMM8_SIGNED | |
| TEST_SUITE_END() // Quantized | |
| TEST_SUITE_END() // NHWC | |
| TEST_SUITE(NCHW) | |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( | |
| framework::dataset::make("InputInfo", { | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Unsupported kernel width | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Non-rectangular weights dimensions are unsupported | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW) // Unsupported stride | |
| }), | |
| framework::dataset::make("WeightsInfo",{ | |
| TensorInfo(TensorShape(11U, 11U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW), | |
| TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW), | |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW) | |
| })), | |
| framework::dataset::make("BiasesInfo",{ | |
| TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW), | |
| TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW), | |
| TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW) | |
| })), | |
| framework::dataset::make("OutputInfo",{ | |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW), | |
| TensorInfo(TensorShape(23U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW), | |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW) | |
| })), | |
| framework::dataset::make("ConvInfo", { | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(1, 1, 0, 0), | |
| PadStrideInfo(3, 3, 0, 0) | |
| })), | |
| framework::dataset::make("ActivationInfo", | |
| { | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) | |
| })), | |
| framework::dataset::make("Expected", { false, false, false})), | |
| input_info, weights_info, biases_info, output_info, conv_info, act_info, expected) | |
| { | |
| bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info)); | |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); | |
| } | |
| // clang-format on | |
| // *INDENT-ON* | |
| TEST_SUITE(Float) | |
| TEST_SUITE(FP16) | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType", DataType::F16)), | |
| ActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", DataLayout::NCHW))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType", DataType::F16)), | |
| ActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", DataLayout::NCHW))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num); | |
| } | |
| TEST_SUITE_END() // FP16 | |
| TEST_SUITE(FP32) | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType", | |
| DataType::F32)), | |
| ActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, | |
| framework::dataset::make("DataType", | |
| DataType::F32)), | |
| ActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType", DataType::F32)), | |
| ActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); | |
| } | |
| TEST_SUITE_END() // FP32 | |
| TEST_SUITE(FP32_CustomDataset) | |
| FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::DirectConvolutionLayerDataset(), | |
| framework::dataset::make("DataType", DataType::F32)), | |
| ActivationFunctionsDataset)) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); | |
| } | |
| TEST_SUITE_END() // FP32_CustomDataset | |
| TEST_SUITE_END() // Float | |
| const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo", | |
| { | |
| ActivationLayerInfo(), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) | |
| }); | |
| TEST_SUITE(Quantized) | |
| TEST_SUITE(QASYMM8) | |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(data_precommit, | |
| framework::dataset::make("DataType", DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(data_precommit, | |
| framework::dataset::make("DataType", DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(data_precommit_9x9, | |
| framework::dataset::make("DataType", | |
| DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(3.f / 255, 10), QuantizationInfo(1.1f, 10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(data_nightly, framework::dataset::make("DataType", | |
| DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(data_nightly_9x9, | |
| framework::dataset::make("DataType", | |
| DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(3.f / 255, 10), QuantizationInfo(1.1f, 10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| TEST_SUITE_END() // QASYMM8 | |
| TEST_SUITE(QASYMM8_CustomDataset) | |
| FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, | |
| combine(combine(combine(combine(datasets::DirectConvolutionLayerDataset(), | |
| framework::dataset::make("DataType", DataType::QASYMM8)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| TEST_SUITE_END() // QASYMM8_CustomDataset | |
| TEST_SUITE(QASYMM8_SIGNED) | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(data_precommit, framework::dataset::make("DataType", | |
| DataType::QASYMM8_SIGNED)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, -10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(data_precommit, | |
| framework::dataset::make("DataType", | |
| DataType::QASYMM8_SIGNED)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.1f, -10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(data_precommit_9x9, | |
| framework::dataset::make("DataType", | |
| DataType::QASYMM8_SIGNED)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunCustomDataset, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY, | |
| combine(combine(combine(combine(datasets::DirectConvolutionLayerDataset(), | |
| framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), | |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) })), | |
| QuantizedActivationFunctionsDataset), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); | |
| } | |
| TEST_SUITE_END() // QASYMM8_SIGNED | |
| TEST_SUITE_END() // Quantized | |
| TEST_SUITE_END() // NCHW | |
| TEST_SUITE_END() // DirectConvolutionLayer | |
| TEST_SUITE_END() // CL | |
| } // namespace validation | |
| } // namespace test | |
| } // namespace arm_compute |