diff options
author | Moritz Pflanzer <moritz.pflanzer@arm.com> | 2017-08-09 11:45:15 +0100 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | 69d3341e6903c1ea87c46e39d6d3e64b2a0d5b4e (patch) | |
tree | fcb9afb7b9c51614bde40c978aa0d50849d27168 /tests/validation | |
parent | 75af28e6936f084fdfa65add7631cd2cd2050b0c (diff) | |
download | ComputeLibrary-69d3341e6903c1ea87c46e39d6d3e64b2a0d5b4e.tar.gz |
COMPMID-415: Move FullyConnectedLayer to new validation
Change-Id: I7f60d6fb484d3962b88874e1531cec734c11e416
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/83556
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'tests/validation')
-rw-r--r-- | tests/validation/CL/FullyConnectedLayer.cpp | 221 | ||||
-rw-r--r-- | tests/validation/NEON/FullyConnectedLayer.cpp | 243 | ||||
-rw-r--r-- | tests/validation/Reference.cpp | 40 | ||||
-rw-r--r-- | tests/validation/Reference.h | 14 | ||||
-rw-r--r-- | tests/validation/ReferenceCPP.cpp | 10 | ||||
-rw-r--r-- | tests/validation/ReferenceCPP.h | 8 | ||||
-rw-r--r-- | tests/validation/TensorOperations.h | 68 | ||||
-rw-r--r-- | tests/validation/TensorVisitors.h | 22 |
8 files changed, 0 insertions, 626 deletions
diff --git a/tests/validation/CL/FullyConnectedLayer.cpp b/tests/validation/CL/FullyConnectedLayer.cpp deleted file mode 100644 index 21ea5a5064..0000000000 --- a/tests/validation/CL/FullyConnectedLayer.cpp +++ /dev/null @@ -1,221 +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 "dataset/FullyConnectedLayerDataset.h" -#include "tests/Globals.h" -#include "tests/Utils.h" -#include "validation/Datasets.h" -#include "validation/Reference.h" -#include "validation/Validation.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h" - -#include <random> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ -const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ - -CLTensor compute_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) -{ - // Create tensors - CLTensor src = create_tensor<CLTensor>(input_shape, dt, 1, fixed_point_position); - CLTensor bias = create_tensor<CLTensor>(bias_shape, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(output_shape, dt, 1, fixed_point_position); - - // Swap the first and second dimension of weights' shape if transpose_weights is true - TensorShape ws = weights_shape; - if(transpose_weights) - { - const size_t dimx = ws.x(); - ws.set(0, ws.y()); - ws.set(1, dimx); - } - - CLTensor weights = create_tensor<CLTensor>(ws, dt, 1, fixed_point_position); - - // Create and configure function. - // Note: We pass the weights already transposed - CLFullyConnectedLayer fc; - fc.configure(&src, &weights, &bias, &dst, false); - - // Allocate tensors - src.allocator()->allocate(); - weights.allocator()->allocate(); - bias.allocator()->allocate(); - dst.allocator()->allocate(); - - BOOST_TEST(!src.info()->is_resizable()); - BOOST_TEST(!weights.info()->is_resizable()); - BOOST_TEST(!bias.info()->is_resizable()); - BOOST_TEST(!dst.info()->is_resizable()); - - // Fill tensors - if(dt == DataType::F32) - { - std::uniform_real_distribution<> distribution(-1.0f, 1.0f); - library->fill(CLAccessor(src), distribution, 0); - library->fill(CLAccessor(weights), distribution, 1); - library->fill(CLAccessor(bias), distribution, 2); - } - else - { - library->fill_tensor_uniform(CLAccessor(src), 0); - library->fill_tensor_uniform(CLAccessor(weights), 1); - library->fill_tensor_uniform(CLAccessor(bias), 2); - } - - // Compute NEFullyConnectedLayer function - fc.run(); - - return dst; -} -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(CL) -BOOST_AUTO_TEST_SUITE(FullyConnectedLayer) - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(Configuration, - SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8, DataType::QS16 }), - fc_set, dt) -{ - // Set fixed point position data type allowed - int fixed_point_position = (dt == DataType::F32) ? 0 : 3; - - // Create tensors - CLTensor src = create_tensor<CLTensor>(fc_set.src_shape, dt, 1, fixed_point_position); - CLTensor bias = create_tensor<CLTensor>(fc_set.bias_shape, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(fc_set.dst_shape, dt, 1, fixed_point_position); - - // Swap the first and second dimension of weights' shape if transpose_weights is true - TensorShape ws = fc_set.weights_shape; - if(fc_set.transpose_weights) - { - const size_t dimx = ws.x(); - ws.set(0, ws.y()); - ws.set(1, dimx); - } - - CLTensor weights = create_tensor<CLTensor>(ws, dt, 1, fixed_point_position); - - BOOST_TEST(src.info()->is_resizable()); - BOOST_TEST(weights.info()->is_resizable()); - BOOST_TEST(bias.info()->is_resizable()); - BOOST_TEST(dst.info()->is_resizable()); - - // Create and configure function. - // Note: We pass the weights already transposed - CLFullyConnectedLayer fc; - fc.configure(&src, &weights, &bias, &dst, false); - - // Validate valid region - const ValidRegion src_valid_region = shape_to_valid_region(fc_set.src_shape); - const ValidRegion weights_valid_region = shape_to_valid_region(ws); - const ValidRegion bias_valid_region = shape_to_valid_region(fc_set.bias_shape); - const ValidRegion dst_valid_region = shape_to_valid_region(fc_set.dst_shape); - - validate(src.info()->valid_region(), src_valid_region); - validate(weights.info()->valid_region(), weights_valid_region); - validate(bias.info()->valid_region(), bias_valid_region); - validate(dst.info()->valid_region(), dst_valid_region); -} - -BOOST_AUTO_TEST_SUITE(Float) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RunSmall, - SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }), - fc_set, dt) -{ - // Compute function - CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_f32); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(RunLarge, - LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }), - fc_set, dt) -{ - // Compute function - CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_f32); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(Quantized) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RunSmall, - SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), - fc_set, dt, fixed_point_position) -{ - // Compute function - CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_q); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(RunLarge, - LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), - fc_set, dt, fixed_point_position) -{ - // Compute function - CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_q); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -#endif // DOXYGEN_SKIP_THIS diff --git a/tests/validation/NEON/FullyConnectedLayer.cpp b/tests/validation/NEON/FullyConnectedLayer.cpp deleted file mode 100644 index 22572ecbf8..0000000000 --- a/tests/validation/NEON/FullyConnectedLayer.cpp +++ /dev/null @@ -1,243 +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 "dataset/FullyConnectedLayerDataset.h" -#include "tests/Globals.h" -#include "tests/Utils.h" -#include "validation/Datasets.h" -#include "validation/Reference.h" -#include "validation/Validation.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" - -#include <random> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ -const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ -#ifdef ARM_COMPUTE_ENABLE_FP16 -const float tolerance_f16 = 0.01f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ -#endif /*ARM_COMPUTE_ENABLE_FP16*/ - -Tensor compute_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) -{ - // Create tensors - Tensor src = create_tensor<Tensor>(input_shape, dt, 1, fixed_point_position); - Tensor bias = create_tensor<Tensor>(bias_shape, dt, 1, fixed_point_position); - Tensor dst = create_tensor<Tensor>(output_shape, dt, 1, fixed_point_position); - - // Swap the first and second dimension of weights' shape if transpose_weights is true - TensorShape ws = weights_shape; - if(transpose_weights) - { - const size_t dimx = ws.x(); - ws.set(0, ws.y()); - ws.set(1, dimx); - } - - Tensor weights = create_tensor<Tensor>(ws, dt, 1, fixed_point_position); - - // Create and configure function. - // Note: We pass the weights already transposed - NEFullyConnectedLayer fc; - fc.configure(&src, &weights, &bias, &dst, false); - - // Allocate tensors - src.allocator()->allocate(); - weights.allocator()->allocate(); - bias.allocator()->allocate(); - dst.allocator()->allocate(); - - BOOST_TEST(!src.info()->is_resizable()); - BOOST_TEST(!weights.info()->is_resizable()); - BOOST_TEST(!bias.info()->is_resizable()); - BOOST_TEST(!dst.info()->is_resizable()); - - // Fill tensors - if(dt == DataType::F16 || dt == DataType::F32) - { - std::uniform_real_distribution<> distribution(-1.0f, 1.0f); - library->fill(Accessor(src), distribution, 0); - library->fill(Accessor(weights), distribution, 1); - library->fill(Accessor(bias), distribution, 2); - } - else - { - library->fill_tensor_uniform(Accessor(src), 0); - library->fill_tensor_uniform(Accessor(weights), 1); - library->fill_tensor_uniform(Accessor(bias), 2); - } - - // Compute NEFullyConnectedLayer function - fc.run(); - - return dst; -} -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(NEON) -BOOST_AUTO_TEST_SUITE(FullyConnectedLayer) - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(Configuration, - SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8, DataType::QS16 }), - fc_set, dt) -{ - // Set fixed point position data type allowed - int fixed_point_position = (dt == DataType::F32) ? 0 : 3; - - // Create tensors - Tensor src = create_tensor<Tensor>(fc_set.src_shape, dt, 1, fixed_point_position); - Tensor bias = create_tensor<Tensor>(fc_set.bias_shape, dt, 1, fixed_point_position); - Tensor dst = create_tensor<Tensor>(fc_set.dst_shape, dt, 1, fixed_point_position); - - // Swap the first and second dimension of weights' shape if transpose_weights is true - TensorShape ws = fc_set.weights_shape; - if(fc_set.transpose_weights) - { - const size_t dimx = ws.x(); - ws.set(0, ws.y()); - ws.set(1, dimx); - } - - Tensor weights = create_tensor<Tensor>(ws, dt, 1, fixed_point_position); - - BOOST_TEST(src.info()->is_resizable()); - BOOST_TEST(weights.info()->is_resizable()); - BOOST_TEST(bias.info()->is_resizable()); - BOOST_TEST(dst.info()->is_resizable()); - - // Create and configure function. - // Note: We pass the weights already transposed - NEFullyConnectedLayer fc; - fc.configure(&src, &weights, &bias, &dst, false); - - // Validate valid region - const ValidRegion src_valid_region = shape_to_valid_region(fc_set.src_shape); - const ValidRegion weights_valid_region = shape_to_valid_region(ws); - const ValidRegion bias_valid_region = shape_to_valid_region(fc_set.bias_shape); - const ValidRegion dst_valid_region = shape_to_valid_region(fc_set.dst_shape); - - validate(src.info()->valid_region(), src_valid_region); - validate(weights.info()->valid_region(), weights_valid_region); - validate(bias.info()->valid_region(), bias_valid_region); - validate(dst.info()->valid_region(), dst_valid_region); -} - -#ifdef ARM_COMPUTE_ENABLE_FP16 -BOOST_AUTO_TEST_SUITE(Float16) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RunSmall, - SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F16 }), - fc_set, dt) -{ - // Compute function - Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_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, - SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }), - fc_set, dt) -{ - // Compute function - Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_f32); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(RunLarge, - LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }), - fc_set, dt) -{ - // Compute function - Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_f32); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(Quantized) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(RunSmall, - SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), - fc_set, dt, fixed_point_position) -{ - // Compute function - Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_q); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(RunLarge, - LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), - fc_set, dt, fixed_point_position) -{ - // Compute function - Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref_dst, tolerance_q); -} -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 16b4cb9217..1ea017e998 100644 --- a/tests/validation/Reference.cpp +++ b/tests/validation/Reference.cpp @@ -461,46 +461,6 @@ RawTensor Reference::compute_reference_batch_normalization_layer(const TensorSha return ref_dst; } -RawTensor Reference::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) -{ - // Create reference - RawTensor ref_src(input_shape, dt, 1, fixed_point_position); - RawTensor ref_bias(bias_shape, dt, 1, fixed_point_position); - RawTensor ref_dst(output_shape, dt, 1, fixed_point_position); - - // Swap the first and second dimension of weights' shape if transpose_weights is true - TensorShape ws = weights_shape; - if(transpose_weights) - { - const size_t dimx = ws.x(); - ws.set(0, ws.y()); - ws.set(1, dimx); - } - - RawTensor ref_weights(ws, dt, 1, fixed_point_position); - - // Fill reference - if(dt == DataType::F16 || dt == DataType::F32) - { - std::uniform_real_distribution<> distribution(-1.0f, 1.0f); - library->fill(ref_src, distribution, 0); - library->fill(ref_weights, distribution, 1); - library->fill(ref_bias, distribution, 2); - } - else - { - library->fill_tensor_uniform(ref_src, 0); - library->fill_tensor_uniform(ref_weights, 1); - library->fill_tensor_uniform(ref_bias, 2); - } - - // Compute reference - ReferenceCPP::fully_connected_layer(ref_src, ref_weights, ref_bias, ref_dst); - - 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 93e12fff26..288dc0e3f7 100644 --- a/tests/validation/Reference.h +++ b/tests/validation/Reference.h @@ -293,20 +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 for fully connected layer function - * - * @param[in] input_shape Shape for the input tensor - * @param[in] weights_shape Shape for the weights tensor - * @param[in] bias_shape Shape for the bias tensor - * @param[in] output_shape Shape for the output tensor - * @param[in] dt Data type to use - * @param[in] transpose_weights Transpose the weights if true - * @param[in] fixed_point_position Number of bits for the fractional part of the fixed point numbers - * - * @return Computed raw tensor. - */ - 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 pooling layer. * * @param[in] shape_in Shape of the input tensor. diff --git a/tests/validation/ReferenceCPP.cpp b/tests/validation/ReferenceCPP.cpp index 3bf70a0a19..58b47f9d81 100644 --- a/tests/validation/ReferenceCPP.cpp +++ b/tests/validation/ReferenceCPP.cpp @@ -281,16 +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); } -// Fully connected layer -void ReferenceCPP::fully_connected_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst) -{ - const TensorVariant s = TensorFactory::get_tensor(src); - const TensorVariant w = TensorFactory::get_tensor(weights); - const TensorVariant b = TensorFactory::get_tensor(bias); - TensorVariant d = TensorFactory::get_tensor(dst); - boost::apply_visitor(tensor_visitors::fully_connected_layer_visitor(s, w, b), d); -} - // Pooling Layer void ReferenceCPP::pooling_layer(const RawTensor &src, RawTensor &dst, PoolingLayerInfo pool_info) { diff --git a/tests/validation/ReferenceCPP.h b/tests/validation/ReferenceCPP.h index ce424bed03..29612d1e3b 100644 --- a/tests/validation/ReferenceCPP.h +++ b/tests/validation/ReferenceCPP.h @@ -259,14 +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); - /** Fully connected layer function - * - * @param[in] src Input tensor - * @param[in] weights Weights tensor. - * @param[in] bias Bias tensor. - * @param[out] dst Result tensor. - */ - static void fully_connected_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst); /** 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 c4884be7ca..f5be139dcf 100644 --- a/tests/validation/TensorOperations.h +++ b/tests/validation/TensorOperations.h @@ -58,52 +58,6 @@ struct is_floating_point { }; -template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> -void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out, int cols_weights, int rows_weights, uint8_t fixed_point_position) -{ - for(int x = 0; x < cols_weights; ++x) - { - T acc(0); - for(int y = 0; y < rows_weights; ++y) - { - acc += in[y] * weights[x + y * cols_weights]; - } - out[x] = acc + bias[x]; - } -} - -// Vector matrix multiply for fixed point type -template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> -void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out, int cols_weights, int rows_weights, uint8_t fixed_point_position) -{ - using namespace fixed_point_arithmetic; - using promoted_type = typename fixed_point_arithmetic::traits::promote<T>::type; - - for(int x = 0; x < cols_weights; ++x) - { - // Reset accumulator - fixed_point<promoted_type> acc(0, fixed_point_position); - - for(int y = 0; y < rows_weights; ++y) - { - const fixed_point<promoted_type> i_value(in[y], fixed_point_position, true); - const fixed_point<promoted_type> w_value(weights[x + y * cols_weights], fixed_point_position, true); - const fixed_point<promoted_type> iw = i_value * w_value; - acc = iw + acc; - } - - // Get the bias - const fixed_point<T> b(bias[x], fixed_point_position, true); - - // Convert back and accumulate the bias - fixed_point<T> res(acc); - res = res + b; - - // Store the result - out[x] = res.raw(); - } -} - // Return a tensor element at a specified coordinate with different border modes template <typename T> T tensor_elem_at(const Tensor<T> &in, Coordinates coord, BorderMode border_mode, T constant_border_value) @@ -1117,28 +1071,6 @@ void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor } } -// Fully connected layer -template <typename T> -void fully_connected_layer(const Tensor<T> &in, const Tensor<T> &weights, const Tensor<T> &bias, Tensor<T> &out) -{ - ARM_COMPUTE_ERROR_ON(weights.shape().x() != out.shape().x()); - ARM_COMPUTE_ERROR_ON(weights.shape().y() != in.shape().x() * in.shape().y() * in.shape().z()); - const int cols_weights = weights.shape().x(); - const int rows_weights = weights.shape().y(); - const int num_batches = in.shape().total_size() / rows_weights; - - for(int k = 0; k < num_batches; ++k) - { - vector_matrix_multiply<T>(in.data() + k * rows_weights, - weights.data(), - bias.data(), - out.data() + k * cols_weights, - cols_weights, - rows_weights, - in.fixed_point_position()); - } -} - // 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) diff --git a/tests/validation/TensorVisitors.h b/tests/validation/TensorVisitors.h index 193697acf0..732cd0e8f1 100644 --- a/tests/validation/TensorVisitors.h +++ b/tests/validation/TensorVisitors.h @@ -232,28 +232,6 @@ private: float _epsilon; int _fixed_point_position; }; -// Fully Connected Layer visitor -struct fully_connected_layer_visitor : public boost::static_visitor<> -{ -public: - explicit fully_connected_layer_visitor(const TensorVariant &in, const TensorVariant &weights, const TensorVariant &bias) - : _in(in), _weights(weights), _bias(bias) - { - } - template <typename T> - void operator()(Tensor<T> &out) const - { - const Tensor<T> &in = boost::get<Tensor<T>>(_in); - const Tensor<T> &weights = boost::get<Tensor<T>>(_weights); - const Tensor<T> &bias = boost::get<Tensor<T>>(_bias); - tensor_operations::fully_connected_layer(in, weights, bias, out); - } - -private: - const TensorVariant &_in; - const TensorVariant &_weights; - const TensorVariant &_bias; -}; // Pooling layer struct pooling_layer_visitor : public boost::static_visitor<> |