diff options
Diffstat (limited to 'tests/validation/NEON/DirectConvolutionLayer.cpp')
| -rw-r--r-- | tests/validation/NEON/DirectConvolutionLayer.cpp | 225 |
1 files changed, 202 insertions, 23 deletions
diff --git a/tests/validation/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp index 05bfbc171a..0779c9d388 100644 --- a/tests/validation/NEON/DirectConvolutionLayer.cpp +++ b/tests/validation/NEON/DirectConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 ARM Limited. + * Copyright (c) 2017-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,10 +21,14 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ +#include "arm_compute/core/Helpers.h" #include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/StringUtils.h" #include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" +#include "src/common/cpuinfo/CpuIsaInfo.h" +#include "src/cpu/kernels/CpuDirectConv2dKernel.h" #include "tests/NEON/Accessor.h" #include "tests/PaddingCalculator.h" #include "tests/datasets/ShapeDatasets.h" @@ -69,8 +73,8 @@ const auto data_pad_f16 = concat(combine(framework::dataset::make("PadX", { 0, 1 framework::dataset::make("KernelSize", 1)))); const auto data_f32 = combine(datasets::SmallDirectConvolutionShapes(), - combine(framework::dataset::make("StrideX", { 1, 2, 3 }), - combine(framework::dataset::make("StrideY", { 1, 2, 3 }), + combine(framework::dataset::make("StrideX", { 1, 2, 3, 4 }), + combine(framework::dataset::make("StrideY", { 1, 2, 3, 4 }), data_pad_f32))); const auto data_f16 = combine(datasets::SmallDirectConvolutionShapes(), @@ -78,25 +82,52 @@ const auto data_f16 = combine(datasets::SmallDirectConvolutionShapes(), combine(framework::dataset::make("StrideY", { 1, 2, 3 }), data_pad_f16))); -const auto data = combine(datasets::SmallDirectConvolutionShapes(), - combine(framework::dataset::make("StrideX", { 1 }), - combine(framework::dataset::make("StrideY", { 1 }), - combine(framework::dataset::make("PadX", { 1 }), - combine(framework::dataset::make("PadY", { 1 }), - framework::dataset::make("KernelSize", 3)))))); +const auto data_prec = combine(datasets::SmallDirectConvolutionShapes(), + combine(framework::dataset::make("StrideX", { 1 }), + combine(framework::dataset::make("StrideY", { 1 }), + combine(framework::dataset::make("PadX", { 1 }), + combine(framework::dataset::make("PadY", { 1 }), + framework::dataset::make("KernelSize", 3)))))); const auto data9x9 = combine(datasets::SmallDirectConvolutionShapes(), - combine(framework::dataset::make("StrideX", { 1 }), - combine(framework::dataset::make("StrideY", { 1 }), + combine(framework::dataset::make("StrideX", { 1, 2, 3 }), + combine(framework::dataset::make("StrideY", { 1, 2, 3 }), combine(framework::dataset::make("PadX", { 0, 2 }), combine(framework::dataset::make("PadY", { 0, 3 }), framework::dataset::make("KernelSize", 9)))))); -const auto data_f32_nightly = combine(data_f32, framework::dataset::make("NumKernels", { 1, 4 })); -const auto data_f16_nightly = combine(data_f16, framework::dataset::make("NumKernels", { 1, 4 })); +const auto data8x8 = combine(datasets::SmallDirectConvolutionShapes(), + combine(framework::dataset::make("StrideX", { 1, 2, 3 }), + combine(framework::dataset::make("StrideY", { 1, 2, 3 }), + combine(framework::dataset::make("PadX", { 0 }), + combine(framework::dataset::make("PadY", { 0 }), + framework::dataset::make("KernelSize", 8)))))); -const auto data_precommit = combine(data, framework::dataset::make("NumKernels", { 1 })); +const auto data_f32_nightly = combine(data_f32, framework::dataset::make("NumKernels", { 1, 4, 5 })); +const auto data_f16_nightly = combine(data_f16, framework::dataset::make("NumKernels", { 1, 4, 5 })); + +const auto data_precommit = combine(data_prec, framework::dataset::make("NumKernels", { 1 })); const auto data_precommit9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 4 })); +const auto data_precommit8x8 = combine(data8x8, framework::dataset::make("NumKernels", { 4 })); + +/* The following tests is from real use-case that made DirectConvolution + * overflows in terms of its tensor indexing. This test case is using + * a separate tolerance due to the following reason. + * - It has shown that it requires generally larger absolute tolerance + * for large numbers or larger relative tolerance for small numbers. + * - With the first reason, since it is mainly testing index overflow, + * a value with a margin is used to avoid uninteded test failures + * during nightly. + */ +constexpr AbsoluteTolerance<float> usecase_tolerance_fp32(0.05f); + +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 }))))))); /** Activation function Dataset*/ const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", @@ -109,17 +140,95 @@ const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo TEST_SUITE(NEON) 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<Tensor>(src_shape, dt); + auto weights = create_tensor<Tensor>(weights_shape, dt); + auto dst = create_tensor<Tensor>(dst_shape, dt); + + const auto conv_info = PadStrideInfo(1, 1, 0, 0); + + // Create Direct Convolution function + NEDirectConvolutionLayer conv{}; + conv.configure(&src, &weights, nullptr, &dst, conv_info); + + src.allocator()->allocate(); + weights.allocator()->allocate(); + dst.allocator()->allocate(); + + library->fill_tensor_value(Accessor(src), 1.f); + library->fill_tensor_value(Accessor(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(Accessor(dst), ref_dst); +} + +DATA_TEST_CASE(KernelSelection, framework::DatasetMode::ALL, + concat(combine(combine(framework::dataset::make("CpuExt", std::string("NEON")), + framework::dataset::make("DataType", { DataType::F32 })), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), + combine(combine(framework::dataset::make("CpuExt", std::string("NEON")), + framework::dataset::make("DataType", { DataType::F16 })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))), + cpu_ext, data_type, data_layout) +{ + using namespace cpu::kernels; + + cpuinfo::CpuIsaInfo cpu_isa{}; + cpu_isa.neon = (cpu_ext == "NEON"); + cpu_isa.fp16 = (data_type == DataType::F16); + + const auto *selected_impl = CpuDirectConv2dKernel::get_implementation(DataTypeDataLayoutISASelectorData{ data_type, data_layout, cpu_isa }, cpu::KernelSelectionType::Preferred); + + ARM_COMPUTE_ERROR_ON_NULLPTR(selected_impl); + + std::string data_layout_str; + if(data_layout == DataLayout::NCHW) + { + data_layout_str = "nchw"; + } + else + { + data_layout_str = "nhwc"; + } + + std::string expected = lower_string(cpu_ext) + "_" + cpu_impl_dt(data_type) + "_" + data_layout_str + "_directconv2d"; + std::string actual = selected_impl->name; + + ARM_COMPUTE_EXPECT_EQUAL(expected, actual, framework::LogLevel::ERRORS); +} + // *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), // Mismatching data type input/weights - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching input feature maps + 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), // Unsupported kernel width - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non-rectangular weights dimensions + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported non-rectangular weights dimensions TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid stride - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported stride + 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 }), framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16), @@ -165,7 +274,14 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( framework::dataset::make("ActivationInfo", { ActivationLayerInfo(), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(), + ActivationLayerInfo(), + ActivationLayerInfo(), + ActivationLayerInfo(), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), })), framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })), input_info, weights_info, biases_info, output_info, conv_info, act_info, expected) @@ -176,10 +292,47 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( // clang-format on // *INDENT-ON* -//TODO(COMPMID-415): Configuration tests? +DATA_TEST_CASE(NoPaddingNHWCKernel, framework::DatasetMode::ALL, combine(combine(combine(data_precommit, + framework::dataset::make("DataType", DataType::F32)), + ActivationFunctionsDataset), + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + + shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, act_info, data_layout) +{ + TensorShape input_shape = TensorShape(shape); + TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels); + const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR); + + TensorInfo input_info = TensorInfo(input_shape, 1, data_type); + TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type); + + TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, info); + + if(data_layout == DataLayout::NHWC) + { + permute(input_shape, PermutationVector(2U, 0U, 1U)); + permute(weights_shape, PermutationVector(2U, 0U, 1U)); + permute(output_shape, PermutationVector(2U, 0U, 1U)); + } + + // Create tensors + Tensor src = create_tensor<Tensor>(input_shape, data_type, 1, QuantizationInfo(), data_layout); + Tensor weights = create_tensor<Tensor>(weights_shape, data_type, 1, QuantizationInfo(), data_layout); + Tensor dst = create_tensor<Tensor>(output_shape, data_type, 1, QuantizationInfo(), data_layout); + + // Create and configure function + NEDirectConvolutionLayer conv; + conv.configure(&src, &weights, nullptr, &dst, info, act_info); + + validate(src.info()->padding(), PaddingSize(0, 0, 0, 0)); + validate(weights.info()->padding(), PaddingSize(0, 0, 0, 0)); + validate(dst.info()->padding(), PaddingSize(0, 0, 0, 0)); +} template <typename T> using NEDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T>; +template <typename T> +using NEDirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T, true>; TEST_SUITE(Float) #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC @@ -211,6 +364,24 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolutionLayerFixture<float>, framewo // Validate output validate(Accessor(_target), _reference, tolerance_fp32); } +FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEDirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, + framework::dataset::make("DataType", DataType::F32)), + ActivationFunctionsDataset), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} + +FIXTURE_DATA_TEST_CASE(RunSmall8x8, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit8x8, framework::dataset::make("DataType", + DataType::F32)), + ActivationFunctionsDataset), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} + FIXTURE_DATA_TEST_CASE(RunSmall9x9, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit9x9, framework::dataset::make("DataType", DataType::F32)), ActivationFunctionsDataset), @@ -227,10 +398,18 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<float>, framewo // Validate output validate(Accessor(_target), _reference, tolerance_fp32); } +FIXTURE_DATA_TEST_CASE(RunLargeUsecase, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly_usecase, framework::dataset::make("DataType", + DataType::F32)), + framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NHWC }))) +{ + // Validate output + validate(Accessor(_target), _reference, usecase_tolerance_fp32); +} TEST_SUITE_END() // FP32 TEST_SUITE_END() // Float TEST_SUITE_END() // DirectConvolutionLayer -TEST_SUITE_END() // NEON +TEST_SUITE_END() // Neon } // namespace validation } // namespace test } // namespace arm_compute |