From 8aaf93e8c12ce93d3d0082d4f4b70376f15536da Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Thu, 11 Oct 2018 17:33:32 +0100 Subject: COMPMID-1632 Add CLL2NormalizationLayer for NHWC and FP32 Change-Id: Iae22554d5fe893fd22a000eab5bfd8275ea06eb3 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/154102 Reviewed-by: Georgios Pinitas Tested-by: bsgcomp --- tests/validation/CL/L2NormalizeLayer.cpp | 34 +++- tests/validation/CL/ReductionOperation.cpp | 6 +- tests/validation/NEON/L2NormalizeLayer.cpp | 8 +- .../validation/fixtures/L2NormalizeLayerFixture.h | 38 ++++- tests/validation/reference/L2NormalizeLayer.cpp | 5 +- tests/validation/reference/ReductionOperation.cpp | 186 ++++++--------------- 6 files changed, 119 insertions(+), 158 deletions(-) (limited to 'tests/validation') diff --git a/tests/validation/CL/L2NormalizeLayer.cpp b/tests/validation/CL/L2NormalizeLayer.cpp index 3d121b079d..517ba84069 100644 --- a/tests/validation/CL/L2NormalizeLayer.cpp +++ b/tests/validation/CL/L2NormalizeLayer.cpp @@ -44,6 +44,10 @@ namespace { /** Tolerance for float operations */ constexpr AbsoluteTolerance tolerance_f32(0.00001f); +constexpr AbsoluteTolerance tolerance_f16(0.01f); + +auto data = concat(combine(framework::dataset::make("DataLayout", { DataLayout::NCHW }), framework::dataset::make("Axis", { 0 })), combine(framework::dataset::make("DataLayout", { DataLayout::NHWC }), + framework::dataset::make("Axis", { 1 }))); } // namespace @@ -58,7 +62,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( TensorInfo(TensorShape(128U, 64U), 2, DataType::F32), // Number of Input channels != 1 TensorInfo(TensorShape(128U, 64U), 1, DataType::S16), // DataType != F32 TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis >= num_max_dimensions - TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis > 0 + TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis > 3 TensorInfo(TensorShape(128U, 64U), 1, DataType::F32) }), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(128U, 64U), 1, DataType::F16), @@ -69,7 +73,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), TensorInfo(TensorShape(128U, 64U), 1, DataType::F32) })), - framework::dataset::make("Axis", { 0U, 0U, 0U, 0U, static_cast(TensorShape::num_max_dimensions), 1U, 0U })), + framework::dataset::make("Axis", { 0U, 0U, 0U, 0U, static_cast(TensorShape::num_max_dimensions), 4U, 0U })), framework::dataset::make("Expected", { false, false, false, false, false, false, true })), input_info, output_info, axis, expected) { @@ -87,22 +91,36 @@ using CLL2NormalizeLayerFixture = L2NormalizeLayerValidationFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0 })), framework::dataset::make("Epsilon", { 1e-12 }))) + combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)), data), framework::dataset::make("Epsilon", { 1e-12 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLL2NormalizeLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0 })), framework::dataset::make("Epsilon", { 1e-12 }))) + combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)), data), framework::dataset::make("Epsilon", { 1e-12 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE_END() // FP32 +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, CLL2NormalizeLayerFixture, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)), data), framework::dataset::make("Epsilon", { 1e-6 }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLL2NormalizeLayerFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F16)), data), framework::dataset::make("Epsilon", { 1e-6 }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() // FP16 +TEST_SUITE_END() // Float -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE_END() // L2NormalizeLayer +TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute diff --git a/tests/validation/CL/ReductionOperation.cpp b/tests/validation/CL/ReductionOperation.cpp index 516a1341cc..2adb4e90d6 100644 --- a/tests/validation/CL/ReductionOperation.cpp +++ b/tests/validation/CL/ReductionOperation.cpp @@ -58,16 +58,16 @@ TEST_SUITE(ReductionOperation) DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("InputInfo", { TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Mismatching data type input/output TensorInfo(TensorShape(128U, 64U), 2, DataType::F32), // Number of Input channels != 1 - TensorInfo(TensorShape(128U, 64U), 1, DataType::S16), // DataType != F16/F32 + TensorInfo(TensorShape(128U, 64U), 1, DataType::S16), // DataType != QASYMM8/F16/F32 TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis >= num_max_dimensions - TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis > 0 and SUM_SQUARE + TensorInfo(TensorShape(128U, 64U), 1, DataType::QASYMM8), // Axis == 0 and SUM_SQUARE and QASYMM8 TensorInfo(TensorShape(128U, 64U), 1, DataType::F32) }), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(1U, 64U), 1, DataType::F16), TensorInfo(TensorShape(1U, 64U), 1, DataType::F32), TensorInfo(TensorShape(1U, 64U), 1, DataType::S16), TensorInfo(TensorShape(1U, 64U), 1, DataType::F32), - TensorInfo(TensorShape(1U, 64U), 1, DataType::F32), + TensorInfo(TensorShape(1U, 64U), 1, DataType::QASYMM8), TensorInfo(TensorShape(1U, 64U), 1, DataType::F32) })), framework::dataset::make("Axis", { 0U, 0U, 0U, static_cast(TensorShape::num_max_dimensions), 1U, 0U })), diff --git a/tests/validation/NEON/L2NormalizeLayer.cpp b/tests/validation/NEON/L2NormalizeLayer.cpp index f868adea3b..0a1ddba77c 100644 --- a/tests/validation/NEON/L2NormalizeLayer.cpp +++ b/tests/validation/NEON/L2NormalizeLayer.cpp @@ -85,14 +85,18 @@ using NEL2NormalizeLayerFixture = L2NormalizeLayerValidationFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0 })), framework::dataset::make("Epsilon", { 1e-12 }))) + combine(combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW })), + framework::dataset::make("Axis", { 0 })), + framework::dataset::make("Epsilon", { 1e-12 }))) { // Validate output validate(Accessor(_target), _reference, tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, NEL2NormalizeLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0 })), framework::dataset::make("Epsilon", { 1e-12 }))) + combine(combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW })), + framework::dataset::make("Axis", { 0 })), + framework::dataset::make("Epsilon", { 1e-12 }))) { // Validate output validate(Accessor(_target), _reference, tolerance_f32); diff --git a/tests/validation/fixtures/L2NormalizeLayerFixture.h b/tests/validation/fixtures/L2NormalizeLayerFixture.h index 6f11dcb658..097d1c4ec2 100644 --- a/tests/validation/fixtures/L2NormalizeLayerFixture.h +++ b/tests/validation/fixtures/L2NormalizeLayerFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -45,10 +45,10 @@ class L2NormalizeLayerValidationFixture : public framework::Fixture { public: template - void setup(TensorShape shape, DataType data_type, unsigned int axis, float epsilon) + void setup(TensorShape shape, DataType data_type, DataLayout data_layout, unsigned int axis, float epsilon) { - _target = compute_target(shape, data_type, axis, epsilon); - _reference = compute_reference(shape, data_type, axis, epsilon); + _target = compute_target(shape, data_type, data_layout, axis, epsilon); + _reference = compute_reference(shape, data_type, data_layout, axis, epsilon); } protected: @@ -58,11 +58,16 @@ protected: library->fill_tensor_uniform(tensor, 0); } - TensorType compute_target(const TensorShape &shape, DataType data_type, unsigned int axis, float epsilon) + TensorType compute_target(TensorShape shape, DataType data_type, DataLayout data_layout, unsigned int axis, float epsilon) { + if(data_layout == DataLayout::NHWC) + { + permute(shape, PermutationVector(2U, 0U, 1U)); + } + // Create tensors - TensorType src = create_tensor(shape, data_type); - TensorType dst = create_tensor(shape, data_type); + TensorType src = create_tensor(shape, data_type, 1, QuantizationInfo(), data_layout); + TensorType dst = create_tensor(shape, data_type, 1, QuantizationInfo(), data_layout); // Create and configure function FunctionType l2_norm_func; @@ -87,8 +92,25 @@ protected: return dst; } - SimpleTensor compute_reference(const TensorShape &shape, DataType data_type, unsigned int axis, float epsilon) + SimpleTensor compute_reference(const TensorShape &shape, DataType data_type, DataLayout data_layout, unsigned int axis, float epsilon) { + if(data_layout == DataLayout::NHWC) + { + switch(axis) + { + case 0: + axis = 2; + break; + case 1: + axis = 0; + break; + case 2: + axis = 1; + break; + default: + break; + } + } // Create reference SimpleTensor src{ shape, data_type }; diff --git a/tests/validation/reference/L2NormalizeLayer.cpp b/tests/validation/reference/L2NormalizeLayer.cpp index 99f4e8a6e6..26677511e4 100644 --- a/tests/validation/reference/L2NormalizeLayer.cpp +++ b/tests/validation/reference/L2NormalizeLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -66,7 +66,7 @@ SimpleTensor l2_normalize(const SimpleTensor &src, unsigned int axis, floa { const T *src_row_ptr = src.data() + du * elems; T *dst_row_ptr = dst.data() + du * elems; - const T normalization_value = std::sqrt(std::max(sum[du], epsilon)); + const T normalization_value = sqrt(std::max(sum[du], static_cast(epsilon))); std::transform(src_row_ptr, src_row_ptr + elems, dst_row_ptr, [normalization_value](T val) { return val / normalization_value; @@ -82,6 +82,7 @@ SimpleTensor l2_normalize(const SimpleTensor &src, unsigned int axis, floa } template SimpleTensor l2_normalize(const SimpleTensor &src, unsigned int axis, float epsilon); +template SimpleTensor l2_normalize(const SimpleTensor &src, unsigned int axis, float epsilon); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation/reference/ReductionOperation.cpp b/tests/validation/reference/ReductionOperation.cpp index 499263f11e..2f103a6f65 100644 --- a/tests/validation/reference/ReductionOperation.cpp +++ b/tests/validation/reference/ReductionOperation.cpp @@ -39,36 +39,39 @@ namespace reference namespace { template -struct square +T reduce_operation(T *ptr, int reduce_elements, ReductionOperation op, int stride) { - T operator()(const T &lhs, const T &rhs) const - { - return (lhs + rhs * rhs); - } -}; + using type = typename std::remove_cv::type; + auto res = type(0); -template -struct sum -{ - T operator()(const T &lhs, const T &rhs) const + if(std::is_integral::value) { - return (lhs + rhs); + uint32_t int_res = 0; + for(int i = 0; i < reduce_elements; ++i) + { + auto elem = static_cast(*(ptr + stride * i)); + int_res += (op == ReductionOperation::SUM_SQUARE) ? elem * elem : elem; + } + if(op == ReductionOperation::MEAN_SUM && reduce_elements > 0) + { + int_res /= reduce_elements; + } + res = saturate_cast(int_res); } -}; - -template -T reduce_operation(T *ptr, int reduce_elements, ReductionOperation op) -{ - switch(op) + else { - case ReductionOperation::SUM_SQUARE: - return std::accumulate(ptr, ptr + reduce_elements, static_cast(0), square()); - case ReductionOperation::SUM: - case ReductionOperation::MEAN_SUM: - return std::accumulate(ptr, ptr + reduce_elements, static_cast(0), sum()); - default: - ARM_COMPUTE_ERROR("Unsupported reduction operation"); + for(int i = 0; i < reduce_elements; ++i) + { + auto elem = *(ptr + stride * i); + res += (op == ReductionOperation::SUM_SQUARE) ? elem * elem : elem; + } + if(op == ReductionOperation::MEAN_SUM && reduce_elements > 0) + { + res /= reduce_elements; + } } + + return res; } } // namespace @@ -77,44 +80,22 @@ SimpleTensor reduction_operation(const SimpleTensor &src, const TensorShap { // Create reference SimpleTensor dst{ dst_shape, src.data_type(), 1, src.quantization_info() }; - const unsigned int src_width = src.shape().x(); - const unsigned int src_height = src.shape().y(); - const unsigned int src_depth = src.shape().z(); - const unsigned int src_batch = src.shape()[3]; - const bool mean = op == ReductionOperation::MEAN_SUM; + const unsigned int src_width = src.shape().x(); + const unsigned int src_height = src.shape().y(); + const unsigned int src_depth = src.shape().z(); + const unsigned int src_batch = src.shape()[3]; + const int reduce_elems = src.shape()[axis]; switch(axis) { case 0: { - const int reduce_elems = src.shape()[axis]; - const unsigned int upper_dims = src.shape().total_size_upper(1); + const unsigned int upper_dims = src.shape().total_size_upper(1); for(unsigned int du = 0; du < upper_dims; ++du) { - if(std::is_integral::value) - { - uint32_t res = 0; - for(unsigned int x = 0; x < src_width; ++x) - { - res += static_cast(src[du * src_width + x]); - } - if(mean && src_width > 0) - { - res /= src_width; - } - dst[du] = saturate_cast(res); - } - else - { - const T *src_row_ptr = src.data() + du * reduce_elems; - - auto res = reduce_operation(src_row_ptr, reduce_elems, op); - if(mean && src_width > 0) - { - res /= src_width; - } - dst[du] = res; - } + const T *src_row_ptr = src.data() + du * reduce_elems; + auto res = reduce_operation(src_row_ptr, reduce_elems, op, 1); + dst[du] = res; } } break; @@ -125,32 +106,11 @@ SimpleTensor reduction_operation(const SimpleTensor &src, const TensorShap { for(unsigned int x = 0; x < src_width; ++x) { - if(std::is_integral::value) - { - uint32_t res = 0; - for(unsigned int y = 0; y < src_height; ++y) - { - res += static_cast(src[du * src_height * src_width + y * src_width + x]); - } - if(mean && src_height > 0) - { - res /= src_height; - } - dst[du * src_width + x] = saturate_cast(res); - } - else - { - auto res = T(0); - for(unsigned int y = 0; y < src_height; ++y) - { - res += src[du * src_height * src_width + y * src_width + x]; - } - if(mean && src_height > 0) - { - res /= src_height; - } - dst[du * src_width + x] = res; - } + const int in_offset = du * src_height * src_width + x; + const int out_offset = du * src_width + x; + const T *src_row_ptr = src.data() + in_offset; + auto res = reduce_operation(src_row_ptr, reduce_elems, op, src_width); + dst[out_offset] = res; } } } @@ -164,32 +124,11 @@ SimpleTensor reduction_operation(const SimpleTensor &src, const TensorShap { for(unsigned int y = 0; y < src_height; ++y) { - if(std::is_integral::value) - { - uint32_t res = T(0); - for(unsigned int z = 0; z < src_depth; ++z) - { - res += static_cast(src[du * src_depth * src_height * src_width + z * src_height * src_width + y * src_width + x]); - } - if(mean && src_depth > 0) - { - res /= src_depth; - } - dst[du * src_width * src_height + y * src_width + x] = saturate_cast(res); - } - else - { - auto res = T(0); - for(unsigned int z = 0; z < src_depth; ++z) - { - res += src[du * src_depth * src_height * src_width + z * src_height * src_width + y * src_width + x]; - } - if(mean && src_depth > 0) - { - res /= src_depth; - } - dst[du * src_width * src_height + y * src_width + x] = res; - } + const int in_offset = du * src_depth * src_height * src_width + y * src_width + x; + const int out_offset = du * src_width * src_height + y * src_width + x; + const T *src_row_ptr = src.data() + in_offset; + auto res = reduce_operation(src_row_ptr, reduce_elems, op, src_height * src_width); + dst[out_offset] = res; } } } @@ -206,34 +145,11 @@ SimpleTensor reduction_operation(const SimpleTensor &src, const TensorShap { for(unsigned int x = 0; x < src_width; ++x) { - if(std::is_integral::value) - { - uint32_t res = 0; - for(unsigned int w = 0; w < src_batch; ++w) - { - res += static_cast(src[du * src_batch * src_depth * src_height * src_width + w * src_width * src_height * src_depth + z * src_width * src_height + y * src_width + x]); - } - if(mean && src_batch > 0) - { - res /= src_batch; - } - - dst[du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x] = saturate_cast(res); - } - else - { - auto res = T(0); - for(unsigned int w = 0; w < src_batch; ++w) - { - res += src[du * src_batch * src_depth * src_height * src_width + w * src_width * src_height * src_depth + z * src_width * src_height + y * src_width + x]; - } - if(mean && src_batch > 0) - { - res /= src_batch; - } - - dst[du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x] = res; - } + const int in_offset = du * src_batch * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x; + const int out_offset = du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x; + const T *src_row_ptr = src.data() + in_offset; + auto res = reduce_operation(src_row_ptr, reduce_elems, op, src_width * src_height * src_depth); + dst[out_offset] = res; } } } -- cgit v1.2.1