diff options
author | Moritz Pflanzer <moritz.pflanzer@arm.com> | 2017-07-19 10:18:42 +0100 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-09-17 14:16:42 +0100 |
commit | 6db73ce5222d4b27b06c4e4aa9e466ceb9a09ba2 (patch) | |
tree | d8649ef21112bd68936904a2008ada1360472320 /tests/validation | |
parent | afde732eb016f18c781923cf1e6c9edf68f586f7 (diff) | |
download | ComputeLibrary-6db73ce5222d4b27b06c4e4aa9e466ceb9a09ba2.tar.gz |
COMPMID-415: Move NormalizationLayer to new validation
Change-Id: Icf5781c920836fe87d2db27ca3f9cc4eb2bea554
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/80999
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Diffstat (limited to 'tests/validation')
-rw-r--r-- | tests/validation/NEON/NormalizationLayer.cpp | 177 | ||||
-rw-r--r-- | tests/validation/Reference.cpp | 24 | ||||
-rw-r--r-- | tests/validation/Reference.h | 10 | ||||
-rw-r--r-- | tests/validation/ReferenceCPP.cpp | 8 | ||||
-rw-r--r-- | tests/validation/ReferenceCPP.h | 7 | ||||
-rw-r--r-- | tests/validation/TensorOperations.h | 202 | ||||
-rw-r--r-- | tests/validation/TensorVisitors.h | 20 |
7 files changed, 0 insertions, 448 deletions
diff --git a/tests/validation/NEON/NormalizationLayer.cpp b/tests/validation/NEON/NormalizationLayer.cpp deleted file mode 100644 index 8a5db369d1..0000000000 --- a/tests/validation/NEON/NormalizationLayer.cpp +++ /dev/null @@ -1,177 +0,0 @@ -/* - * Copyright (c) 2017 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 "NEON/Accessor.h" -#include "TypePrinter.h" -#include "tests/Globals.h" -#include "tests/Utils.h" -#include "validation/Datasets.h" -#include "validation/Reference.h" -#include "validation/Validation.h" - -#include "arm_compute/runtime/NEON/functions/NENormalizationLayer.h" - -#include <random> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -/** Define tolerance of the normalization layer depending on values data type. - * - * @param[in] dt Data type of the tensors' values. - * - * @return Tolerance depending on the data type. - */ -float normalization_layer_tolerance(DataType dt) -{ - switch(dt) - { - case DataType::QS8: - return 2.0f; - case DataType::F16: - return 0.001f; - case DataType::F32: - return 1e-05; - default: - return 0.f; - } -} - -/** Compute Neon normalization layer function. - * - * @param[in] shape Shape of the input and output tensors. - * @param[in] dt Data type of input and output tensors. - * @param[in] norm_info Normalization Layer information. - * @param[in] fixed_point_position (Optional) Fixed point position that expresses the number of bits for the fractional part of the number when the tensor's data type is QS8 or QS16 (default = 0). - * - * @return Computed output tensor. - */ -Tensor compute_normalization_layer(const TensorShape &shape, DataType dt, NormalizationLayerInfo norm_info, int fixed_point_position = 0) -{ - // Create tensors - Tensor src = create_tensor<Tensor>(shape, dt, 1, fixed_point_position); - Tensor dst = create_tensor<Tensor>(shape, dt, 1, fixed_point_position); - - // Create and configure function - NENormalizationLayer norm; - norm.configure(&src, &dst, norm_info); - - // Allocate tensors - src.allocator()->allocate(); - dst.allocator()->allocate(); - - BOOST_TEST(!src.info()->is_resizable()); - BOOST_TEST(!dst.info()->is_resizable()); - - // Fill tensors - if(dt == DataType::QS8) - { - const int8_t one_fixed_point = 1 << fixed_point_position; - const int8_t minus_one_fixed_point = -one_fixed_point; - library->fill_tensor_uniform(Accessor(src), 0, minus_one_fixed_point, one_fixed_point); - } - else - { - library->fill_tensor_uniform(Accessor(src), 0); - } - - // Compute function - norm.run(); - - return dst; -} -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(NEON) -BOOST_AUTO_TEST_SUITE(NormalizationLayer) - -#ifdef ARM_COMPUTE_ENABLE_FP16 -BOOST_AUTO_TEST_SUITE(Float16) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RunSmall, - SmallShapes() * DataType::F16 *NormalizationTypes() * boost::unit_test::data::xrange(3, 9, 2) * boost::unit_test::data::make({ 0.5f, 1.0f, 2.0f }), - shape, dt, norm_type, norm_size, beta) -{ - // Provide normalization layer information - NormalizationLayerInfo norm_info(norm_type, norm_size, 5, beta); - - // Compute function - Tensor dst = compute_normalization_layer(shape, dt, norm_info); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_normalization_layer(shape, dt, norm_info); - - // Validate output - validate(Accessor(dst), ref_dst, normalization_layer_tolerance(DataType::F16)); -} - -BOOST_AUTO_TEST_SUITE_END() -#endif /* ARM_COMPUTE_ENABLE_FP16 */ - -BOOST_AUTO_TEST_SUITE(Float) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RunSmall, - SmallShapes() * DataType::F32 *NormalizationTypes() * boost::unit_test::data::xrange(3, 9, 2) * boost::unit_test::data::make({ 0.5f, 1.0f, 2.0f }), - shape, dt, norm_type, norm_size, beta) -{ - // Provide normalization layer information - NormalizationLayerInfo norm_info(norm_type, norm_size, 5, beta); - - // Compute function - Tensor dst = compute_normalization_layer(shape, dt, norm_info); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_normalization_layer(shape, dt, norm_info); - - // Validate output - validate(Accessor(dst), ref_dst, normalization_layer_tolerance(DataType::F32)); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(Quantized) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RunSmall, - SmallShapes() * DataType::QS8 *NormalizationTypes() * boost::unit_test::data::xrange(3, 7, 2) * (boost::unit_test::data::xrange(1, 6) * boost::unit_test::data::make({ 0.5f, 1.0f, 2.0f })), - shape, dt, norm_type, norm_size, fixed_point_position, beta) -{ - // Provide normalization layer information - NormalizationLayerInfo norm_info(norm_type, norm_size, 5, beta, 1.f); - - // Compute function - Tensor dst = compute_normalization_layer(shape, dt, norm_info, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_normalization_layer(shape, dt, norm_info, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref_dst, normalization_layer_tolerance(DataType::QS8)); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -#endif /* DOXYGEN_SKIP_THIS */ diff --git a/tests/validation/Reference.cpp b/tests/validation/Reference.cpp index 5c669903c8..b7553f3b7b 100644 --- a/tests/validation/Reference.cpp +++ b/tests/validation/Reference.cpp @@ -660,30 +660,6 @@ RawTensor Reference::compute_reference_fully_connected_layer(const TensorShape & return ref_dst; } -RawTensor Reference::compute_reference_normalization_layer(const TensorShape &shape, DataType dt, NormalizationLayerInfo norm_info, int fixed_point_position) -{ - // Create reference - RawTensor ref_src(shape, dt, 1, fixed_point_position); - RawTensor ref_dst(shape, dt, 1, fixed_point_position); - - // Fill reference - if(dt == DataType::QS8) - { - const int8_t one_fixed_point = 1 << fixed_point_position; - const int8_t minus_one_fixed_point = -one_fixed_point; - library->fill_tensor_uniform(ref_src, 0, minus_one_fixed_point, one_fixed_point); - } - else - { - library->fill_tensor_uniform(ref_src, 0); - } - - // Compute reference - ReferenceCPP::normalization_layer(ref_src, ref_dst, norm_info); - - return ref_dst; -} - RawTensor Reference::compute_reference_pooling_layer(const TensorShape &shape_in, const TensorShape &shape_out, DataType dt, PoolingLayerInfo pool_info, int fixed_point_position) { // Create reference diff --git a/tests/validation/Reference.h b/tests/validation/Reference.h index 034a308327..778e7b0b2b 100644 --- a/tests/validation/Reference.h +++ b/tests/validation/Reference.h @@ -353,16 +353,6 @@ public: */ static RawTensor compute_reference_fully_connected_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt, bool transpose_weights, int fixed_point_position); - /** Compute reference normalization layer. - * - * @param[in] shape Shape of the input and output tensors. - * @param[in] dt Data type of input and output tensors. - * @param[in] norm_info Normalization Layer information. - * @param[in] fixed_point_position (Optional) Fixed point position that expresses the number of bits for the fractional part of the number when the tensor's data type is QS8 or QS16 (default = 0). - * - * @return Computed raw tensor. - */ - static RawTensor compute_reference_normalization_layer(const TensorShape &shape, DataType dt, NormalizationLayerInfo norm_info, int fixed_point_position = 0); /** Compute reference pooling layer. * * @param[in] shape_in Shape of the input tensor. diff --git a/tests/validation/ReferenceCPP.cpp b/tests/validation/ReferenceCPP.cpp index dd2437195e..13f4b90a82 100644 --- a/tests/validation/ReferenceCPP.cpp +++ b/tests/validation/ReferenceCPP.cpp @@ -337,14 +337,6 @@ void ReferenceCPP::fully_connected_layer(const RawTensor &src, const RawTensor & boost::apply_visitor(tensor_visitors::fully_connected_layer_visitor(s, w, b), d); } -// Normalization Layer -void ReferenceCPP::normalization_layer(const RawTensor &src, RawTensor &dst, NormalizationLayerInfo norm_info) -{ - const TensorVariant s = TensorFactory::get_tensor(src); - TensorVariant d = TensorFactory::get_tensor(dst); - boost::apply_visitor(tensor_visitors::normalization_layer_visitor(s, norm_info), d); -} - // Pooling Layer void ReferenceCPP::pooling_layer(const RawTensor &src, RawTensor &dst, PoolingLayerInfo pool_info, int fixed_point_position) { diff --git a/tests/validation/ReferenceCPP.h b/tests/validation/ReferenceCPP.h index 6d4d243c95..3f5e4aeaf5 100644 --- a/tests/validation/ReferenceCPP.h +++ b/tests/validation/ReferenceCPP.h @@ -296,13 +296,6 @@ public: * @param[out] dst Result tensor. */ static void fully_connected_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst); - /** Normalization of @p src based on the information from @p norm_info. - * - * @param[in] src Input tensor. - * @param[out] dst Result tensor. - * @param[in] norm_info Normalization Layer information. - */ - static void normalization_layer(const RawTensor &src, RawTensor &dst, NormalizationLayerInfo norm_info); /** Pooling layer of @p src based on the information from @p pool_info. * * @param[in] src Input tensor. diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h index 5018bfdb2b..a5039a4641 100644 --- a/tests/validation/TensorOperations.h +++ b/tests/validation/TensorOperations.h @@ -1207,208 +1207,6 @@ void fully_connected_layer(const Tensor<T> &in, const Tensor<T> &weights, const } } -// Normalization Layer for floating point type -template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> -void normalization_layer(const Tensor<T> &in, Tensor<T> &out, NormalizationLayerInfo norm_info) -{ - const uint32_t norm_size = norm_info.norm_size(); - NormType type = norm_info.type(); - float beta = norm_info.beta(); - uint32_t kappa = norm_info.kappa(); - - const int cols = static_cast<int>(in.shape()[0]); - const int rows = static_cast<int>(in.shape()[1]); - const int depth = static_cast<int>(in.shape()[2]); - int upper_dims = in.shape().total_size() / (cols * rows); - - float coeff = norm_info.scale_coeff(); - int radius_cols = norm_size / 2; - // IN_MAP_1D and CROSS_MAP normalize over a single axis only - int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0; - - if(type == NormType::CROSS_MAP) - { - // Remove also depth from upper dimensions since it is the axes we want - // to use for normalization - upper_dims /= depth; - for(int r = 0; r < upper_dims; ++r) - { - for(int i = 0; i < rows; ++i) - { - for(int k = 0; k < cols; ++k) - { - for(int l = 0; l < depth; ++l) - { - float accumulated_scale = 0.f; - for(int j = -radius_cols; j <= radius_cols; ++j) - { - const int z = l + j; - if(z >= 0 && z < depth) - { - const T value = in[k + i * cols + z * rows * cols + r * cols * rows * depth]; - accumulated_scale += value * value; - } - } - out[k + i * cols + l * rows * cols + r * cols * rows * depth] = kappa + accumulated_scale * coeff; - } - } - } - } - } - else - { - for(int r = 0; r < upper_dims; ++r) - { - for(int i = 0; i < rows; ++i) - { - for(int k = 0; k < cols; ++k) - { - float accumulated_scale = 0.f; - for(int j = -radius_rows; j <= radius_rows; ++j) - { - const int y = i + j; - for(int l = -radius_cols; l <= radius_cols; ++l) - { - const int x = k + l; - if((x >= 0 && y >= 0) && (x < cols && y < rows)) - { - const T value = in[x + y * cols + r * cols * rows]; - accumulated_scale += value * value; - } - } - } - out[k + i * cols + r * cols * rows] = kappa + accumulated_scale * coeff; - } - } - } - } - - if(beta == 1.f) - { - for(int i = 0; i < out.num_elements(); ++i) - { - out[i] = in[i] / out[i]; - } - } - else if(beta == 0.5f) - { - for(int i = 0; i < out.num_elements(); ++i) - { - out[i] = in[i] / std::sqrt(out[i]); - } - } - else - { - for(int i = 0; i < out.num_elements(); ++i) - { - out[i] = in[i] * std::exp(std::log(out[i]) * -beta); - } - } -} -// Normalization Layer for fixed-point types -template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> -void normalization_layer(const Tensor<T> &in, Tensor<T> &out, NormalizationLayerInfo norm_info) -{ - using namespace fixed_point_arithmetic; - - const int fixed_point_position = in.fixed_point_position(); - - const uint32_t norm_size = norm_info.norm_size(); - NormType type = norm_info.type(); - fixed_point<T> beta(norm_info.beta(), fixed_point_position); - fixed_point<T> kappa(norm_info.kappa(), fixed_point_position); - - const int cols = static_cast<int>(in.shape()[0]); - const int rows = static_cast<int>(in.shape()[1]); - const int depth = static_cast<int>(in.shape()[2]); - int upper_dims = in.shape().total_size() / (cols * rows); - - fixed_point<T> coeff(norm_info.scale_coeff(), fixed_point_position); - int radius_cols = norm_size / 2; - // IN_MAP_1D and CROSS_MAP normalize over a single axis only - int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0; - - if(type == NormType::CROSS_MAP) - { - // Remove also depth from upper dimensions since it is the axes we want - // to use for normalization - upper_dims /= depth; - for(int r = 0; r < upper_dims; ++r) - { - for(int i = 0; i < rows; ++i) - { - for(int k = 0; k < cols; ++k) - { - for(int l = 0; l < depth; ++l) - { - fixed_point<T> accumulated_scale(0.f, fixed_point_position); - for(int j = -radius_cols; j <= radius_cols; ++j) - { - const int z = l + j; - if(z >= 0 && z < depth) - { - const T value = in[k + i * cols + z * rows * cols + r * cols * rows * depth]; - const fixed_point<T> fp_value(value, fixed_point_position, true); - accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value)); - } - } - accumulated_scale = add(kappa, mul(accumulated_scale, coeff)); - out[k + i * cols + l * rows * cols + r * cols * rows * depth] = accumulated_scale.raw(); - } - } - } - } - } - else - { - for(int r = 0; r < upper_dims; ++r) - { - for(int i = 0; i < rows; ++i) - { - for(int k = 0; k < cols; ++k) - { - fixed_point<T> accumulated_scale(0.f, fixed_point_position); - for(int j = -radius_rows; j <= radius_rows; ++j) - { - const int y = i + j; - for(int l = -radius_cols; l <= radius_cols; ++l) - { - const int x = k + l; - if((x >= 0 && y >= 0) && (x < cols && y < rows)) - { - const T value = in[x + y * cols + r * cols * rows]; - const fixed_point<T> fp_value(value, fixed_point_position, true); - accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value)); - } - } - } - accumulated_scale = add(kappa, mul(accumulated_scale, coeff)); - out[k + i * cols + r * cols * rows] = accumulated_scale.raw(); - } - } - } - } - - if(norm_info.beta() == 1.f) - { - for(int i = 0; i < out.num_elements(); ++i) - { - fixed_point<T> res = div(fixed_point<T>(in[i], fixed_point_position, true), fixed_point<T>(out[i], fixed_point_position, true)); - out[i] = res.raw(); - } - } - else - { - const fixed_point<T> beta(norm_info.beta(), fixed_point_position); - for(int i = 0; i < out.num_elements(); ++i) - { - fixed_point<T> res = pow(fixed_point<T>(out[i], fixed_point_position, true), beta); - res = div(fixed_point<T>(in[i], fixed_point_position, true), res); - out[i] = res.raw(); - } - } -} - // Pooling layer template <typename T> void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_info, int fixed_point_position) diff --git a/tests/validation/TensorVisitors.h b/tests/validation/TensorVisitors.h index bccb70a1d3..fa9c3ecbb8 100644 --- a/tests/validation/TensorVisitors.h +++ b/tests/validation/TensorVisitors.h @@ -345,26 +345,6 @@ private: const TensorVariant &_bias; }; -// Normalization Layer visitor -struct normalization_layer_visitor : public boost::static_visitor<> -{ -public: - explicit normalization_layer_visitor(const TensorVariant &in, NormalizationLayerInfo norm_info) - : _in(in), _norm_info(norm_info) - { - } - - template <typename T> - void operator()(Tensor<T> &out) const - { - const Tensor<T> &in = boost::get<Tensor<T>>(_in); - tensor_operations::normalization_layer(in, out, _norm_info); - } - -private: - const TensorVariant &_in; - NormalizationLayerInfo _norm_info; -}; // Pooling layer struct pooling_layer_visitor : public boost::static_visitor<> { |