diff options
Diffstat (limited to 'tests/validation')
-rw-r--r-- | tests/validation/CL/PoolingLayer.cpp | 175 | ||||
-rw-r--r-- | tests/validation/Datasets.h | 7 | ||||
-rw-r--r-- | tests/validation/NEON/PoolingLayer.cpp | 209 | ||||
-rw-r--r-- | tests/validation/Reference.cpp | 33 | ||||
-rw-r--r-- | tests/validation/Reference.h | 11 | ||||
-rw-r--r-- | tests/validation/ReferenceCPP.cpp | 8 | ||||
-rw-r--r-- | tests/validation/ReferenceCPP.h | 7 | ||||
-rw-r--r-- | tests/validation/TensorOperations.h | 223 | ||||
-rw-r--r-- | tests/validation/TensorVisitors.h | 21 |
9 files changed, 0 insertions, 694 deletions
diff --git a/tests/validation/CL/PoolingLayer.cpp b/tests/validation/CL/PoolingLayer.cpp deleted file mode 100644 index 286b1d98df..0000000000 --- a/tests/validation/CL/PoolingLayer.cpp +++ /dev/null @@ -1,175 +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 "CL/CLAccessor.h" -#include "TypePrinter.h" -#include "arm_compute/runtime/CL/functions/CLPoolingLayer.h" -#include "tests/Globals.h" -#include "tests/Utils.h" -#include "tests/dataset/PoolingLayerDataset.h" -#include "validation/Datasets.h" -#include "validation/Reference.h" -#include "validation/Validation.h" - -#include <random> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -const float tolerance_qs8 = 3; /**< Tolerance value for comparing reference's output against implementation's output for quantized input */ -const float tolerance_qs16 = 6; /**< Tolerance value for comparing reference's output against implementation's output for quantized input */ -const float tolerance_f = 1e-05; /**< Tolerance value for comparing reference's output against implementation's output for float input */ - -/** Compute CL pooling layer function. - * - * @param[in] shape Shape of the input and output tensors. - * @param[in] dt Data type of input and output tensors. - * @param[in] pool_info Pooling Layer information. - * @param[in] fixed_point_position The fixed point position. - * - * @return Computed output tensor. - */ -CLTensor compute_pooling_layer(const TensorShape &shape_in, const TensorShape &shape_out, DataType dt, PoolingLayerInfo pool_info, int fixed_point_position = 0) -{ - // Create tensors - CLTensor src = create_tensor<CLTensor>(shape_in, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(shape_out, dt, 1, fixed_point_position); - - // Create and configure function - CLPoolingLayer pool; - pool.configure(&src, &dst, pool_info); - - // Allocate tensors - src.allocator()->allocate(); - dst.allocator()->allocate(); - - BOOST_TEST(!src.info()->is_resizable()); - BOOST_TEST(!dst.info()->is_resizable()); - - // Fill tensors - // Fill tensors - int min = 0; - int max = 0; - switch(dt) - { - case DataType::F32: - min = -1; - max = 1; - break; - case DataType::QS8: - case DataType::QS16: - min = -(1 << fixed_point_position); - max = (1 << fixed_point_position); - break; - default: - ARM_COMPUTE_ERROR("DataType not supported."); - } - std::uniform_real_distribution<> distribution(min, max); - library->fill(CLAccessor(src), distribution, 0); - - // Compute function - pool.run(); - - return dst; -} - -TensorShape get_output_shape(TensorShape in_shape, const PoolingLayerInfo &pool_info) -{ - TensorShape out_shape(in_shape); - const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(), - in_shape.y(), - pool_info.pool_size(), - pool_info.pool_size(), - pool_info.pad_stride_info()); - out_shape.set(0, scaled_dims.first); - out_shape.set(1, scaled_dims.second); - return out_shape; -} -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(CL) -BOOST_AUTO_TEST_SUITE(PoolingLayer) - -BOOST_AUTO_TEST_SUITE(Float) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RunSmall, SmallShapes() * CNNFloatDataTypes() * PoolingTypes() * boost::unit_test::data::make({ 2, 3, 7 }) * boost::unit_test::data::make({ 1, 2 }) * boost::unit_test::data::make({ 0, 1 }), - src_shape, dt, pool_type, pool_size, pool_stride, pool_pad) -{ - PoolingLayerInfo pool_info(pool_type, pool_size, PadStrideInfo(pool_stride, pool_stride, pool_pad, pool_pad, DimensionRoundingType::CEIL)); - TensorShape dst_shape = get_output_shape(src_shape, pool_info); - - // Compute function - CLTensor dst = compute_pooling_layer(src_shape, dst_shape, dt, pool_info); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_pooling_layer(src_shape, dst_shape, dt, pool_info); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_f); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(Quantized) - -BOOST_AUTO_TEST_SUITE(QS8) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RandomDataset, - RandomPoolingLayerDataset() * boost::unit_test::data::xrange(1, 5), - obj, fixed_point_position) -{ - // Compute function - CLTensor dst = compute_pooling_layer(obj.src_shape, obj.dst_shape, DataType::QS8, obj.info, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_pooling_layer(obj.src_shape, obj.dst_shape, DataType::QS8, obj.info, fixed_point_position); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_qs8, 0); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(QS16) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RandomDataset, - RandomPoolingLayerDataset() * boost::unit_test::data::xrange(1, 12), - obj, fixed_point_position) -{ - // Compute function - CLTensor dst = compute_pooling_layer(obj.src_shape, obj.dst_shape, DataType::QS16, obj.info, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_pooling_layer(obj.src_shape, obj.dst_shape, DataType::QS16, obj.info, fixed_point_position); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_qs16, 0); -} -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -#endif /* DOXYGEN_SKIP_THIS */ diff --git a/tests/validation/Datasets.h b/tests/validation/Datasets.h index 64918fc4f5..15e1b098e6 100644 --- a/tests/validation/Datasets.h +++ b/tests/validation/Datasets.h @@ -37,7 +37,6 @@ #include "dataset/MatrixPatternDataset.h" #include "dataset/NonLinearFilterFunctionDataset.h" #include "dataset/NormalizationTypeDataset.h" -#include "dataset/PoolingLayerDataset.h" #include "dataset/PoolingTypesDataset.h" #include "dataset/RoundingPolicyDataset.h" #include "dataset/ShapeDatasets.h" @@ -177,12 +176,6 @@ struct is_dataset<arm_compute::test::NormalizationTypes> : boost::mpl::true_ /// Register the data set with Boost template <> -struct is_dataset<arm_compute::test::RandomPoolingLayerDataset> : boost::mpl::true_ -{ -}; - -/// Register the data set with Boost -template <> struct is_dataset<arm_compute::test::RoundingPolicies> : boost::mpl::true_ { }; diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp deleted file mode 100644 index 8b4ff18f8c..0000000000 --- a/tests/validation/NEON/PoolingLayer.cpp +++ /dev/null @@ -1,209 +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 "arm_compute/runtime/NEON/functions/NEPoolingLayer.h" -#include "tests/Globals.h" -#include "tests/Utils.h" -#include "tests/dataset/PoolingLayerDataset.h" -#include "validation/Datasets.h" -#include "validation/Reference.h" -#include "validation/Validation.h" - -#include <random> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -const float tolerance_q = 0; /**< Tolerance value for comparing reference's output against implementation's output for quantized input */ -const float tolerance_f32 = 1e-05; /**< Tolerance value for comparing reference's output against implementation's output for float input */ -#ifdef ARM_COMPUTE_ENABLE_FP16 -const float tolerance_f16 = 0.001f; /**< Tolerance value for comparing reference's output against half precision floating point implementation's output */ -#endif /* ARM_COMPUTE_ENABLE_FP16 */ - -/** Compute Neon pooling layer function. - * - * @param[in] shape Shape of the input and output tensors. - * @param[in] dt Data type of input and output tensors. - * @param[in] pool_info Pooling Layer information. - * - * @return Computed output tensor. - */ -Tensor compute_pooling_layer(const TensorShape &shape_in, const TensorShape &shape_out, DataType dt, PoolingLayerInfo pool_info, int fixed_point_position = 0) -{ - // Create tensors - Tensor src = create_tensor<Tensor>(shape_in, dt, 1, fixed_point_position); - Tensor dst = create_tensor<Tensor>(shape_out, dt, 1, fixed_point_position); - - // Create and configure function - NEPoolingLayer pool; - pool.configure(&src, &dst, pool_info); - - // Allocate tensors - src.allocator()->allocate(); - dst.allocator()->allocate(); - - BOOST_TEST(!src.info()->is_resizable()); - BOOST_TEST(!dst.info()->is_resizable()); - - // Fill tensors - int min = 0; - int max = 0; - switch(dt) - { - case DataType::F32: - case DataType::F16: - min = -1; - max = 1; - break; - case DataType::QS8: - case DataType::QS16: - min = -(1 << fixed_point_position); - max = (1 << fixed_point_position); - break; - default: - ARM_COMPUTE_ERROR("DataType not supported."); - } - std::uniform_real_distribution<> distribution(min, max); - library->fill(Accessor(src), distribution, 0); - - // Compute function - pool.run(); - - return dst; -} - -TensorShape get_output_shape(TensorShape in_shape, const PoolingLayerInfo &pool_info) -{ - TensorShape out_shape(in_shape); - const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(), - in_shape.y(), - pool_info.pool_size(), - pool_info.pool_size(), - pool_info.pad_stride_info()); - out_shape.set(0, scaled_dims.first); - out_shape.set(1, scaled_dims.second); - return out_shape; -} -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(NEON) -BOOST_AUTO_TEST_SUITE(PoolingLayer) - -BOOST_AUTO_TEST_SUITE(Float) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RandomDataset, - RandomPoolingLayerDataset() * boost::unit_test::data::make(DataType::F32), - obj, dt) -{ - // Compute function - Tensor dst = compute_pooling_layer(obj.src_shape, obj.dst_shape, dt, obj.info); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_pooling_layer(obj.src_shape, obj.dst_shape, dt, obj.info); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_f32, 0); -} - -BOOST_DATA_TEST_CASE(RunSmall7x7, - SmallShapes() * CNNFloatDataTypes() * PoolingTypes() * boost::unit_test::data::make({ 2, 3, 7 }) * boost::unit_test::data::make({ 1, 2 }) * boost::unit_test::data::make({ 0, 1 }), - src_shape, dt, pool_type, pool_size, pool_stride, pool_pad) -{ - PoolingLayerInfo pool_info(pool_type, pool_size, PadStrideInfo(pool_stride, pool_stride, pool_pad, pool_pad, DimensionRoundingType::CEIL)); - TensorShape dst_shape = get_output_shape(src_shape, pool_info); - - // Compute function - Tensor dst = compute_pooling_layer(src_shape, dst_shape, dt, pool_info); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_pooling_layer(src_shape, dst_shape, dt, pool_info); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_f32, 0); -} -BOOST_AUTO_TEST_SUITE_END() - -#ifdef ARM_COMPUTE_ENABLE_FP16 -BOOST_AUTO_TEST_SUITE(Float16) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RandomDataset, - RandomPoolingLayerDataset() * boost::unit_test::data::make(DataType::F16), - obj, dt) -{ - // Compute function - Tensor dst = compute_pooling_layer(obj.src_shape, obj.dst_shape, dt, obj.info); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_pooling_layer(obj.src_shape, obj.dst_shape, dt, obj.info); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_f16, 0); -} -BOOST_AUTO_TEST_SUITE_END() -#endif /* ARM_COMPUTE_ENABLE_FP16 */ - -BOOST_AUTO_TEST_SUITE(Quantized) -BOOST_AUTO_TEST_SUITE(QS8) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RandomDataset, - RandomPoolingLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(1, 5), - obj, dt, fixed_point_position) -{ - // Compute function - Tensor dst = compute_pooling_layer(obj.src_shape, obj.dst_shape, dt, obj.info, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_pooling_layer(obj.src_shape, obj.dst_shape, dt, obj.info, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_q, 0); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(QS16) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RandomDataset, - RandomPoolingLayerDataset() * boost::unit_test::data::make(DataType::QS16) * boost::unit_test::data::xrange(1, 13), - obj, dt, fixed_point_position) -{ - // Compute function - Tensor dst = compute_pooling_layer(obj.src_shape, obj.dst_shape, dt, obj.info, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_pooling_layer(obj.src_shape, obj.dst_shape, dt, obj.info, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_q, 0); -} -BOOST_AUTO_TEST_SUITE_END() -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 1ea017e998..6da92116da 100644 --- a/tests/validation/Reference.cpp +++ b/tests/validation/Reference.cpp @@ -461,39 +461,6 @@ RawTensor Reference::compute_reference_batch_normalization_layer(const TensorSha 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 - RawTensor ref_src(shape_in, dt, 1, fixed_point_position); - RawTensor ref_dst(shape_out, dt, 1, fixed_point_position); - - // Fill reference - int min = 0; - int max = 0; - switch(dt) - { - case DataType::F32: - case DataType::F16: - min = -1; - max = 1; - break; - case DataType::QS8: - case DataType::QS16: - min = -(1 << fixed_point_position); - max = (1 << fixed_point_position); - break; - default: - ARM_COMPUTE_ERROR("DataType not supported."); - } - std::uniform_real_distribution<> distribution(min, max); - library->fill(ref_src, distribution, 0.0); - - // Compute reference - ReferenceCPP::pooling_layer(ref_src, ref_dst, pool_info); - - return ref_dst; -} - RawTensor Reference::compute_reference_roi_pooling_layer(const TensorShape &shape, DataType dt, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info) { TensorShape shape_dst; diff --git a/tests/validation/Reference.h b/tests/validation/Reference.h index 288dc0e3f7..430c42321f 100644 --- a/tests/validation/Reference.h +++ b/tests/validation/Reference.h @@ -293,17 +293,6 @@ public: * @return Computed raw tensor. */ static RawTensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0); - /** Compute reference pooling layer. - * - * @param[in] shape_in Shape of the input tensor. - * @param[in] shape_out Shape of the output tensor. - * @param[in] dt Data type of input and output tensors. - * @param[in] pool_info Pooling Layer information. - * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers. - * - * @return Computed raw tensor. - */ - static RawTensor compute_reference_pooling_layer(const TensorShape &shape_in, const TensorShape &shape_out, DataType dt, PoolingLayerInfo pool_info, int fixed_point_position = 0); /** Compute reference roi pooling layer. * * @param[in] shape Shape of the input tensor. diff --git a/tests/validation/ReferenceCPP.cpp b/tests/validation/ReferenceCPP.cpp index 58b47f9d81..4c831ebe0a 100644 --- a/tests/validation/ReferenceCPP.cpp +++ b/tests/validation/ReferenceCPP.cpp @@ -281,14 +281,6 @@ void ReferenceCPP::batch_normalization_layer(const RawTensor &src, RawTensor &ds boost::apply_visitor(tensor_visitors::batch_normalization_layer_visitor(s, m, v, b, g, epsilon, fixed_point_position), d); } -// Pooling Layer -void ReferenceCPP::pooling_layer(const RawTensor &src, RawTensor &dst, PoolingLayerInfo pool_info) -{ - const TensorVariant s = TensorFactory::get_tensor(src); - TensorVariant d = TensorFactory::get_tensor(dst); - boost::apply_visitor(tensor_visitors::pooling_layer_visitor(s, pool_info), d); -} - // ROI Pooling Layer void ReferenceCPP::roi_pooling_layer(const RawTensor &src, RawTensor &dst, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info) { diff --git a/tests/validation/ReferenceCPP.h b/tests/validation/ReferenceCPP.h index 29612d1e3b..96aade9705 100644 --- a/tests/validation/ReferenceCPP.h +++ b/tests/validation/ReferenceCPP.h @@ -259,13 +259,6 @@ public: */ static void batch_normalization_layer(const RawTensor &src, RawTensor &dst, const RawTensor &mean, const RawTensor &var, const RawTensor &beta, const RawTensor &gamma, float epsilon, int fixed_point_position = 0); - /** Pooling layer of @p src based on the information from @p pool_info. - * - * @param[in] src Input tensor. - * @param[out] dst Result tensor. - * @param[in] pool_info Pooling Layer information. - */ - static void pooling_layer(const RawTensor &src, RawTensor &dst, PoolingLayerInfo pool_info); /** ROI Pooling layer of @p src based on the information from @p pool_info and @p rois. * * @param[in] src Input tensor. diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h index f5be139dcf..e68a344112 100644 --- a/tests/validation/TensorOperations.h +++ b/tests/validation/TensorOperations.h @@ -1071,229 +1071,6 @@ void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor } } -// Pooling layer -template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> -void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_info) -{ - const int pool_size = pool_info.pool_size(); - PoolingType type = pool_info.pool_type(); - int pool_stride_x = 0; - int pool_stride_y = 0; - int pad_x = 0; - int pad_y = 0; - std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info().stride(); - std::tie(pad_x, pad_y) = pool_info.pad_stride_info().pad(); - - const int w_in = static_cast<int>(in.shape()[0]); - const int h_in = static_cast<int>(in.shape()[1]); - - const int w_out = static_cast<int>(out.shape()[0]); - const int h_out = static_cast<int>(out.shape()[1]); - - int upper_dims = in.shape().total_size() / (w_in * h_in); - - int pooled_w = 0; - int pooled_h = 0; - if(pool_info.pad_stride_info().round() == DimensionRoundingType::CEIL) - { - pooled_w = static_cast<int>(ceil(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1; - pooled_h = static_cast<int>(ceil(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1; - } - else - { - pooled_w = static_cast<int>(floor(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1; - pooled_h = static_cast<int>(floor(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1; - } - - if((pooled_w - 1) * pool_stride_x >= w_in + pad_x) - { - --pooled_w; - } - if((pooled_h - 1) * pool_stride_y >= h_in + pad_y) - { - --pooled_h; - } - - if(type == PoolingType::MAX) - { - for(int r = 0; r < upper_dims; ++r) - { - for(int h = 0; h < pooled_h; ++h) - { - for(int w = 0; w < pooled_w; ++w) - { - int wstart = w * pool_stride_x - pad_x; - int hstart = h * pool_stride_y - pad_y; - int wend = std::min(wstart + pool_size, w_in); - int hend = std::min(hstart + pool_size, h_in); - wstart = std::max(wstart, 0); - hstart = std::max(hstart, 0); - - T max_val = std::numeric_limits<T>::lowest(); - for(int y = hstart; y < hend; ++y) - { - for(int x = wstart; x < wend; ++x) - { - const T val = in[r * h_in * w_in + y * w_in + x]; - if(val > max_val) - { - max_val = val; - } - } - } - - out[r * h_out * w_out + h * pooled_w + w] = max_val; - } - } - } - } - else // Average pooling - { - for(int r = 0; r < upper_dims; ++r) - { - for(int h = 0; h < pooled_h; ++h) - { - for(int w = 0; w < pooled_w; ++w) - { - T avg_val(0); - int wstart = w * pool_stride_x - pad_x; - int hstart = h * pool_stride_y - pad_y; - int wend = std::min(wstart + pool_size, w_in + pad_x); - int hend = std::min(hstart + pool_size, h_in + pad_y); - int pool = (hend - hstart) * (wend - wstart); - wstart = std::max(wstart, 0); - hstart = std::max(hstart, 0); - wend = std::min(wend, w_in); - hend = std::min(hend, h_in); - - for(int y = hstart; y < hend; ++y) - { - for(int x = wstart; x < wend; ++x) - { - avg_val += in[r * h_in * w_in + y * w_in + x]; - } - } - out[r * h_out * w_out + h * pooled_w + w] = avg_val / pool; - } - } - } - } -} - -// Pooling layer -template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> -void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_info) -{ - const int pool_size = pool_info.pool_size(); - PoolingType type = pool_info.pool_type(); - int pool_stride_x = 0; - int pool_stride_y = 0; - int pad_x = 0; - int pad_y = 0; - std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info().stride(); - std::tie(pad_x, pad_y) = pool_info.pad_stride_info().pad(); - - const int w_in = static_cast<int>(in.shape()[0]); - const int h_in = static_cast<int>(in.shape()[1]); - - const int w_out = static_cast<int>(out.shape()[0]); - const int h_out = static_cast<int>(out.shape()[1]); - - int upper_dims = in.shape().total_size() / (w_in * h_in); - - int pooled_w = 0; - int pooled_h = 0; - if(pool_info.pad_stride_info().round() == DimensionRoundingType::CEIL) - { - pooled_w = static_cast<int>(ceil(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1; - pooled_h = static_cast<int>(ceil(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1; - } - else - { - pooled_w = static_cast<int>(floor(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1; - pooled_h = static_cast<int>(floor(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1; - } - - if((pooled_w - 1) * pool_stride_x >= w_in + pad_x) - { - --pooled_w; - } - if((pooled_h - 1) * pool_stride_y >= h_in + pad_y) - { - --pooled_h; - } - - if(type == PoolingType::MAX) - { - for(int r = 0; r < upper_dims; ++r) - { - for(int h = 0; h < pooled_h; ++h) - { - for(int w = 0; w < pooled_w; ++w) - { - int wstart = w * pool_stride_x - pad_x; - int hstart = h * pool_stride_y - pad_y; - int wend = std::min(wstart + pool_size, w_in); - int hend = std::min(hstart + pool_size, h_in); - wstart = std::max(wstart, 0); - hstart = std::max(hstart, 0); - - T max_val = std::numeric_limits<T>::lowest(); - for(int y = hstart; y < hend; ++y) - { - for(int x = wstart; x < wend; ++x) - { - T val = in[r * h_in * w_in + y * w_in + x]; - if(val > max_val) - { - max_val = val; - } - } - } - - out[r * h_out * w_out + h * pooled_w + w] = max_val; - } - } - } - } - else // Average pooling - { - for(int r = 0; r < upper_dims; ++r) - { - for(int h = 0; h < pooled_h; ++h) - { - for(int w = 0; w < pooled_w; ++w) - { - int wstart = w * pool_stride_x - pad_x; - int hstart = h * pool_stride_y - pad_y; - int wend = std::min(wstart + pool_size, w_in + pad_x); - int hend = std::min(hstart + pool_size, h_in + pad_y); - int pool = (hend - hstart) * (wend - wstart); - wstart = std::max(wstart, 0); - hstart = std::max(hstart, 0); - wend = std::min(wend, w_in); - hend = std::min(hend, h_in); - - using namespace fixed_point_arithmetic; - - const int fixed_point_position = in.fixed_point_position(); - const fixed_point<T> invpool_fp(1.f / static_cast<float>(pool), fixed_point_position); - fixed_point<T> avg_val(0, fixed_point_position, true); - for(int y = hstart; y < hend; ++y) - { - for(int x = wstart; x < wend; ++x) - { - const fixed_point<T> in_fp(in[r * h_in * w_in + y * w_in + x], fixed_point_position, true); - avg_val = add(avg_val, in_fp); - } - } - out[r * h_out * w_out + h * pooled_w + w] = mul(avg_val, invpool_fp).raw(); - } - } - } - } -} - // ROI Pooling layer template <typename T> void roi_pooling_layer(const Tensor<T> &in, Tensor<T> &out, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info) diff --git a/tests/validation/TensorVisitors.h b/tests/validation/TensorVisitors.h index 732cd0e8f1..a15d2ad1dd 100644 --- a/tests/validation/TensorVisitors.h +++ b/tests/validation/TensorVisitors.h @@ -233,27 +233,6 @@ private: int _fixed_point_position; }; -// Pooling layer -struct pooling_layer_visitor : public boost::static_visitor<> -{ -public: - explicit pooling_layer_visitor(const TensorVariant &in, PoolingLayerInfo pool_info) - : _in(in), _pool_info(pool_info) - { - } - - template <typename T> - void operator()(Tensor<T> &out) const - { - const Tensor<T> &in = boost::get<Tensor<T>>(_in); - tensor_operations::pooling_layer(in, out, _pool_info); - } - -private: - const TensorVariant &_in; - PoolingLayerInfo _pool_info; -}; - // ROI Pooling layer struct roi_pooling_layer_visitor : public boost::static_visitor<> { |