diff options
-rw-r--r-- | tests/datasets_new/FullyConnectedLayerDataset.h | 34 | ||||
-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 | ||||
-rw-r--r-- | tests/validation_new/CL/FullyConnectedLayer.cpp | 205 | ||||
-rw-r--r-- | tests/validation_new/CPP/FullyConnectedLayer.cpp | 133 | ||||
-rw-r--r-- | tests/validation_new/CPP/FullyConnectedLayer.h | 44 | ||||
-rw-r--r-- | tests/validation_new/NEON/FullyConnectedLayer.cpp | 211 | ||||
-rw-r--r-- | tests/validation_new/fixtures/FullyConnectedLayerFixture.h | 249 |
14 files changed, 875 insertions, 627 deletions
diff --git a/tests/datasets_new/FullyConnectedLayerDataset.h b/tests/datasets_new/FullyConnectedLayerDataset.h index 562295f00f..8401e39ece 100644 --- a/tests/datasets_new/FullyConnectedLayerDataset.h +++ b/tests/datasets_new/FullyConnectedLayerDataset.h @@ -59,7 +59,7 @@ public: description << "In=" << *_src_it << ":"; description << "Weights=" << *_weights_it << ":"; description << "Biases=" << *_biases_it << ":"; - description << "Out=" << *_dst_it << ":"; + description << "Out=" << *_dst_it; return description.str(); } @@ -113,6 +113,38 @@ private: std::vector<TensorShape> _bias_shapes{}; std::vector<TensorShape> _dst_shapes{}; }; + +class SmallFullyConnectedLayerDataset final : public FullyConnectedLayerDataset +{ +public: + SmallFullyConnectedLayerDataset() + { + // Conv -> FC + add_config(TensorShape(9U, 5U, 7U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U)); + // Conv -> FC (batched) + add_config(TensorShape(9U, 5U, 7U, 3U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U, 3U)); + // FC -> FC + add_config(TensorShape(201U), TensorShape(201U, 529U), TensorShape(529U), TensorShape(529U)); + // FC -> FC (batched) + add_config(TensorShape(201U, 3U), TensorShape(201U, 529U), TensorShape(529U), TensorShape(529U, 3U)); + + add_config(TensorShape(9U, 5U, 7U, 3U, 2U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U, 3U, 2U)); + } +}; + +class LargeFullyConnectedLayerDataset final : public FullyConnectedLayerDataset +{ +public: + LargeFullyConnectedLayerDataset() + { + add_config(TensorShape(9U, 5U, 257U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U)); + add_config(TensorShape(9U, 5U, 257U, 2U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U, 2U)); + add_config(TensorShape(3127U), TensorShape(3127U, 989U), TensorShape(989U), TensorShape(989U)); + add_config(TensorShape(3127U, 2U), TensorShape(3127U, 989U), TensorShape(989U), TensorShape(989U, 2U)); + + add_config(TensorShape(9U, 5U, 257U, 2U, 3U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U, 2U, 3U)); + } +}; } // namespace datasets } // namespace test } // namespace arm_compute 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<> diff --git a/tests/validation_new/CL/FullyConnectedLayer.cpp b/tests/validation_new/CL/FullyConnectedLayer.cpp new file mode 100644 index 0000000000..9bf3a75d88 --- /dev/null +++ b/tests/validation_new/CL/FullyConnectedLayer.cpp @@ -0,0 +1,205 @@ +/* + * 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 "arm_compute/core/Types.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/CLTensorAllocator.h" +#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h" +#include "framework/Asserts.h" +#include "framework/Macros.h" +#include "framework/datasets/Datasets.h" +#include "tests/CL/CLAccessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets_new/FullyConnectedLayerDataset.h" +#include "tests/validation_new/Validation.h" +#include "tests/validation_new/fixtures/FullyConnectedLayerFixture.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +/** Tolerance for float operations */ +constexpr AbsoluteTolerance<float> tolerance_f32(0.001f); +constexpr AbsoluteTolerance<float> tolerance_f16(0.4f); +/** Tolerance for fixed point operations */ +constexpr AbsoluteTolerance<float> tolerance_fixed_point(1.f); + +/** CNN data types */ +const auto CNNDataTypes = framework::dataset::make("DataType", +{ + DataType::F16, + DataType::F32, + DataType::QS8, + DataType::QS16, +}); + +const auto FullyConnectedParameters = combine(framework::dataset::make("TransposeWeights", { false, true }), framework::dataset::make("ReshapeWeights", { false, true })); +} // namespace + +TEST_SUITE(CL) +TEST_SUITE(FullyConnectedLayer) + +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallFullyConnectedLayerDataset(), datasets::LargeFullyConnectedLayerDataset()), + FullyConnectedParameters), + CNNDataTypes), + src_shape, weights_shape, bias_shape, dst_shape, transpose_weights, reshape_weights, data_type) +{ + // Set fixed point position data type allowed + int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0; + + TensorShape ws(weights_shape); + + // Transpose weights if not done in the function + if(!reshape_weights || !transpose_weights) + { + const size_t shape_x = ws.x(); + ws.set(0, ws.y()); + ws.set(1, shape_x); + + // Weights have to be passed reshaped + // Transpose 1xW for batched version + if(!reshape_weights && dst_shape.y() > 1) + { + const float transpose_width = 16.0f / data_size_from_type(data_type); + const size_t shape_x = ws.x(); + ws.set(0, ws.y() * static_cast<unsigned int>(transpose_width)); + ws.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width))); + } + } + + // Create tensors + CLTensor src = create_tensor<CLTensor>(src_shape, data_type, 1, fixed_point_position); + CLTensor weights = create_tensor<CLTensor>(ws, data_type, 1, fixed_point_position); + CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type, 1, fixed_point_position); + CLTensor dst = create_tensor<CLTensor>(dst_shape, data_type, 1, fixed_point_position); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function. + CLFullyConnectedLayer fc; + fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights); + + // Validate valid region + const ValidRegion dst_valid_region = shape_to_valid_region(dst_shape); + validate(dst.info()->valid_region(), dst_valid_region); +} + +template <typename T> +using CLFullyConnectedLayerFixture = FullyConnectedLayerValidationFixture<CLTensor, CLAccessor, CLFullyConnectedLayer, T>; + +TEST_SUITE(Float) +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixture<half_float::half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixture<half_float::half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() +TEST_SUITE_END() + +template <typename T> +using CLFullyConnectedLayerFixedPointFixture = FullyConnectedLayerValidationFixedPointFixture<CLTensor, CLAccessor, CLFullyConnectedLayer, T>; + +TEST_SUITE(Quantized) +TEST_SUITE(QS8) +// Testing for fixed point position [1,6) as reciprocal limits the maximum fixed point position to 5 +FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 1, 6))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_fixed_point); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 1, 6))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_fixed_point); +} +TEST_SUITE_END() + +TEST_SUITE(QS16) +// Testing for fixed point position [1,14) as reciprocal limits the maximum fixed point position to 14 +FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_fixed_point); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_fixed_point); +} +TEST_SUITE_END() +TEST_SUITE_END() + +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/CPP/FullyConnectedLayer.cpp b/tests/validation_new/CPP/FullyConnectedLayer.cpp new file mode 100644 index 0000000000..7852dab27b --- /dev/null +++ b/tests/validation_new/CPP/FullyConnectedLayer.cpp @@ -0,0 +1,133 @@ +/* + * 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 "FullyConnectedLayer.h" + +#include "tests/validation_new/FixedPoint.h" +#include "tests/validation_new/half.h" + +#include <numeric> + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +namespace +{ +// Vector matrix multiply for floating point +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0> +void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position) +{ + ARM_COMPUTE_UNUSED(fixed_point_position); + + for(int y = 0; y < rows_weights; ++y) + { + dst[y] = std::inner_product(src, src + cols_weights, weights, static_cast<T>(0)) + bias[y]; + weights += cols_weights; + } +} + +// Vector matrix multiply for fixed point type +template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0> +void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position) +{ + using namespace fixed_point_arithmetic; + using promoted_type = fixed_point_arithmetic::traits::promote_t<T>; + + for(int y = 0; y < rows_weights; ++y) + { + // Reset accumulator + fixed_point<promoted_type> acc(0, fixed_point_position); + + for(int x = 0; x < cols_weights; ++x) + { + const fixed_point<promoted_type> i_value(src[x], fixed_point_position, true); + const fixed_point<promoted_type> w_value(weights[x], fixed_point_position, true); + acc = acc + i_value * w_value; + } + + // Get the bias + const fixed_point<T> b(bias[y], fixed_point_position, true); + + // Convert back and accumulate the bias + fixed_point<T> res(acc); + res = res + b; + + // Store the result + dst[y] = res.raw(); + + weights += cols_weights; + } +} +} // namespace + +template <typename T> +SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &dst_shape) +{ + // Create reference + SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position() }; + + // Sanity checks + const int num_batch_dimensions = std::max(0, static_cast<int>(dst_shape.num_dimensions()) - 1); + const int num_input_dimensions = src.shape().num_dimensions() - num_batch_dimensions; + const unsigned int linear_input_size = src.shape().total_size_lower(num_input_dimensions); + + ARM_COMPUTE_UNUSED(num_batch_dimensions); + ARM_COMPUTE_UNUSED(num_input_dimensions); + ARM_COMPUTE_UNUSED(linear_input_size); + ARM_COMPUTE_ERROR_ON(weights.shape().x() != linear_input_size); + ARM_COMPUTE_ERROR_ON(weights.shape().y() != bias.shape().x()); + ARM_COMPUTE_ERROR_ON(weights.shape().y() != dst.shape().x()); + + // Compute reference + const int cols_weights = weights.shape().x(); + const int rows_weights = weights.shape().y(); + const int num_batches = dst_shape.total_size_upper(1); + + for(int k = 0; k < num_batches; ++k) + { + vector_matrix_multiply<T>(src.data() + k * cols_weights, + weights.data(), + bias.data(), + dst.data() + k * rows_weights, + cols_weights, + rows_weights, + src.fixed_point_position()); + } + + return dst; +} + +template SimpleTensor<float> fully_connected_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &dst_shape); +template SimpleTensor<half_float::half> fully_connected_layer(const SimpleTensor<half_float::half> &src, const SimpleTensor<half_float::half> &weights, const SimpleTensor<half_float::half> &bias, + const TensorShape &dst_shape); +template SimpleTensor<qint8_t> fully_connected_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &dst_shape); +template SimpleTensor<qint16_t> fully_connected_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &dst_shape); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/CPP/FullyConnectedLayer.h b/tests/validation_new/CPP/FullyConnectedLayer.h new file mode 100644 index 0000000000..5d62179f57 --- /dev/null +++ b/tests/validation_new/CPP/FullyConnectedLayer.h @@ -0,0 +1,44 @@ +/* + * 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. + */ +#ifndef __ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_H__ +#define __ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_H__ + +#include "tests/SimpleTensor.h" +#include "tests/validation_new/Helpers.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +template <typename T> +SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &dst_shape); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* __ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_H__ */ diff --git a/tests/validation_new/NEON/FullyConnectedLayer.cpp b/tests/validation_new/NEON/FullyConnectedLayer.cpp new file mode 100644 index 0000000000..6eb18ebc6a --- /dev/null +++ b/tests/validation_new/NEON/FullyConnectedLayer.cpp @@ -0,0 +1,211 @@ +/* + * 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 "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/TensorAllocator.h" +#include "framework/Asserts.h" +#include "framework/Macros.h" +#include "framework/datasets/Datasets.h" +#include "tests/NEON/Accessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets_new/FullyConnectedLayerDataset.h" +#include "tests/validation_new/Validation.h" +#include "tests/validation_new/fixtures/FullyConnectedLayerFixture.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +/** Tolerance for float operations */ +constexpr AbsoluteTolerance<float> tolerance_f32(0.001f); +#ifdef ARM_COMPUTE_ENABLE_FP16 +constexpr AbsoluteTolerance<float> tolerance_f16(0.01f); +#endif /* ARM_COMPUTE_ENABLE_FP16*/ +/** Tolerance for fixed point operations */ +constexpr AbsoluteTolerance<float> tolerance_fixed_point(1.f); + +/** CNN data types */ +const auto CNNDataTypes = framework::dataset::make("DataType", +{ +#ifdef ARM_COMPUTE_ENABLE_FP16 + DataType::F16, +#endif /* ARM_COMPUTE_ENABLE_FP16 */ + DataType::F32, + DataType::QS8, + DataType::QS16, +}); + +const auto FullyConnectedParameters = combine(framework::dataset::make("TransposeWeights", { false, true }), framework::dataset::make("ReshapeWeights", { false, true })); +} // namespace + +TEST_SUITE(NEON) +TEST_SUITE(FullyConnectedLayer) + +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallFullyConnectedLayerDataset(), datasets::LargeFullyConnectedLayerDataset()), + FullyConnectedParameters), + CNNDataTypes), + src_shape, weights_shape, bias_shape, dst_shape, transpose_weights, reshape_weights, data_type) +{ + // Set fixed point position data type allowed + int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0; + + TensorShape ws(weights_shape); + + // Transpose weights if not done in the function + if(!reshape_weights || !transpose_weights) + { + const size_t shape_x = ws.x(); + ws.set(0, ws.y()); + ws.set(1, shape_x); + + // Weights have to be passed reshaped + // Transpose 1xW for batched version + if(!reshape_weights && dst_shape.y() > 1) + { + const float transpose_width = 16.0f / data_size_from_type(data_type); + const size_t shape_x = ws.x(); + ws.set(0, ws.y() * static_cast<unsigned int>(transpose_width)); + ws.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width))); + } + } + + // Create tensors + Tensor src = create_tensor<Tensor>(src_shape, data_type, 1, fixed_point_position); + Tensor weights = create_tensor<Tensor>(ws, data_type, 1, fixed_point_position); + Tensor bias = create_tensor<Tensor>(bias_shape, data_type, 1, fixed_point_position); + Tensor dst = create_tensor<Tensor>(dst_shape, data_type, 1, fixed_point_position); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function. + NEFullyConnectedLayer fc; + fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights); + + // Validate valid region + const ValidRegion dst_valid_region = shape_to_valid_region(dst_shape); + validate(dst.info()->valid_region(), dst_valid_region); +} + +template <typename T> +using NEFullyConnectedLayerFixture = FullyConnectedLayerValidationFixture<Tensor, Accessor, NEFullyConnectedLayer, T>; + +TEST_SUITE(Float) +#ifdef ARM_COMPUTE_ENABLE_FP16 +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<half_float::half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<half_float::half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() +#endif /* ARM_COMPUTE_ENABLE_FP16 */ + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() +TEST_SUITE_END() + +template <typename T> +using NEFullyConnectedLayerFixedPointFixture = FullyConnectedLayerValidationFixedPointFixture<Tensor, Accessor, NEFullyConnectedLayer, T>; + +TEST_SUITE(Quantized) +TEST_SUITE(QS8) +// Testing for fixed point position [1,6) as reciprocal limits the maximum fixed point position to 5 +FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 1, 6))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fixed_point); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 1, 6))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fixed_point); +} +TEST_SUITE_END() + +TEST_SUITE(QS16) +// Testing for fixed point position [1,14) as reciprocal limits the maximum fixed point position to 14 +FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fixed_point); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(), + FullyConnectedParameters), + framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fixed_point); +} +TEST_SUITE_END() +TEST_SUITE_END() + +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/fixtures/FullyConnectedLayerFixture.h b/tests/validation_new/fixtures/FullyConnectedLayerFixture.h new file mode 100644 index 0000000000..eb4aad8952 --- /dev/null +++ b/tests/validation_new/fixtures/FullyConnectedLayerFixture.h @@ -0,0 +1,249 @@ +/* + * 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. + */ +#ifndef ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE +#define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "framework/Asserts.h" +#include "framework/Fixture.h" +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/IAccessor.h" +#include "tests/RawTensor.h" +#include "tests/validation_new/CPP/FullyConnectedLayer.h" +#include "tests/validation_new/Helpers.h" + +#include <random> + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +RawTensor transpose(const RawTensor &src, int interleave = 1) +{ + // Create reference + TensorShape dst_shape(src.shape()); + dst_shape.set(0, src.shape().y() * interleave); + dst_shape.set(1, std::ceil(src.shape().x() / static_cast<float>(interleave))); + + RawTensor dst{ dst_shape, src.data_type() }; + + // Compute reference + uint8_t *out_ptr = dst.data(); + + for(int i = 0; i < dst.num_elements(); i += interleave) + { + Coordinates coord = index2coord(dst.shape(), i); + size_t coord_x = coord.x(); + coord.set(0, coord.y() * interleave); + coord.set(1, coord_x / interleave); + + const int num_elements = std::min<int>(interleave, src.shape().x() - coord.x()); + + std::copy_n(static_cast<const uint8_t *>(src(coord)), num_elements * src.element_size(), out_ptr); + + out_ptr += interleave * dst.element_size(); + } + + return dst; +} +} // namespace + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class FullyConnectedLayerValidationFixedPointFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, int fractional_bits) + { + ARM_COMPUTE_UNUSED(weights_shape); + ARM_COMPUTE_UNUSED(bias_shape); + + _fractional_bits = fractional_bits; + _data_type = data_type; + + _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits); + _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + if(is_data_type_float(_data_type)) + { + std::uniform_real_distribution<> distribution(0.5f, 1.f); + library->fill(tensor, distribution, i); + } + else + { + library->fill_tensor_uniform(tensor, i); + } + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights, + bool reshape_weights, DataType data_type, int fixed_point_position) + { + TensorShape reshaped_weights_shape(weights_shape); + + // Test actions depending on the target settings + // + // | reshape | !reshape + // -----------+-----------+--------------------------- + // transpose | | *** + // -----------+-----------+--------------------------- + // !transpose | transpose | transpose & + // | | transpose1xW (if required) + // + // ***: That combination is invalid. But we can ignore the transpose flag and handle all !reshape the same + if(!reshape_weights || !transpose_weights) + { + const size_t shape_x = reshaped_weights_shape.x(); + reshaped_weights_shape.set(0, reshaped_weights_shape.y()); + reshaped_weights_shape.set(1, shape_x); + + // Weights have to be passed reshaped + // Transpose 1xW for batched version + if(!reshape_weights && output_shape.y() > 1) + { + const int transpose_width = 16 / data_size_from_type(data_type); + const float shape_x = reshaped_weights_shape.x(); + reshaped_weights_shape.set(0, reshaped_weights_shape.y() * transpose_width); + reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width))); + } + } + + // Create tensors + TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position); + TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position); + TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position); + TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position); + + // Create and configure function. + FunctionType fc; + fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + weights.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), 0); + fill(AccessorType(bias), 2); + + if(!reshape_weights || !transpose_weights) + { + TensorShape tmp_shape(weights_shape); + RawTensor tmp(tmp_shape, data_type, 1, fixed_point_position); + + // Fill with original shape + fill(tmp, 1); + + // Transpose elementwise + tmp = transpose(tmp); + + // Reshape weights for batched runs + if(!reshape_weights && output_shape.y() > 1) + { + // Transpose with interleave + const int interleave_size = 16 / tmp.element_size(); + tmp = transpose(tmp, interleave_size); + } + + AccessorType weights_accessor(weights); + + for(int i = 0; i < tmp.num_elements(); ++i) + { + Coordinates coord = index2coord(tmp.shape(), i); + std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)), + tmp.element_size(), + static_cast<RawTensor::value_type *>(weights_accessor(coord))); + } + } + else + { + fill(AccessorType(weights), 1); + } + + // Compute NEFullyConnectedLayer function + fc.run(); + + return dst; + } + + SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights, + bool reshape_weights, DataType data_type, int fixed_point_position = 0) + { + // Create reference + SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position }; + SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position }; + SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position }; + + // Fill reference + fill(src, 0); + fill(weights, 1); + fill(bias, 2); + + return reference::fully_connected_layer<T>(src, weights, bias, output_shape); + } + + TensorType _target{}; + SimpleTensor<T> _reference{}; + int _fractional_bits{}; + DataType _data_type{}; +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type) + { + FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, + 0); + } +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */ |