247 lines
11 KiB
C++
247 lines
11 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE
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#define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/core/Utils.h"
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#include "tests/AssetsLibrary.h"
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#include "tests/Globals.h"
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#include "tests/IAccessor.h"
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#include "tests/RawTensor.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Fixture.h"
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#include "tests/validation/Helpers.h"
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#include "tests/validation/reference/ActivationLayer.h"
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#include "tests/validation/reference/FullyConnectedLayer.h"
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#include "tests/validation/reference/Utils.h"
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#include <random>
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class FullyConnectedLayerValidationGenericFixture : public framework::Fixture
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{
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public:
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using TDecay = typename std::decay<T>::type;
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using TBias = typename std::conditional < (std::is_same<TDecay, uint8_t>::value || std::is_same<TDecay, int8_t>::value), int32_t, T >::type;
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public:
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template <typename...>
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void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights,
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DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo activation_info)
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{
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ARM_COMPUTE_UNUSED(weights_shape);
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ARM_COMPUTE_UNUSED(bias_shape);
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_data_type = data_type;
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_bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
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_quantization_info = quantization_info;
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_activation_info = activation_info;
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_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights);
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_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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if(_data_type == DataType::QASYMM8)
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{
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std::uniform_int_distribution<uint8_t> distribution(0, 30);
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library->fill(tensor, distribution, i);
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}
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else if(_data_type == DataType::QASYMM8_SIGNED)
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{
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std::uniform_int_distribution<int8_t> distribution(-15, 15);
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library->fill(tensor, distribution, i);
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}
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else if(_data_type == DataType::S32)
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{
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std::uniform_int_distribution<int32_t> distribution(-50, 50);
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library->fill(tensor, distribution, i);
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}
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else if(is_data_type_float(_data_type))
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{
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std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
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library->fill(tensor, distribution, i);
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}
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else
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{
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library->fill_tensor_uniform(tensor, i);
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}
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}
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TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
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bool reshape_weights)
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{
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TensorShape reshaped_weights_shape(weights_shape);
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// Test actions depending on the target settings
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//
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// | reshape | !reshape
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// -----------+-----------+---------------------------
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// transpose | | ***
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// -----------+-----------+---------------------------
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// !transpose | transpose | transpose
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// | |
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//
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// ***: That combination is invalid. But we can ignore the transpose flag and handle all !reshape the same
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if(!reshape_weights || !transpose_weights)
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{
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const size_t shape_x = reshaped_weights_shape.x();
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reshaped_weights_shape.set(0, reshaped_weights_shape.y());
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reshaped_weights_shape.set(1, shape_x);
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}
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// Create tensors
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TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info);
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TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _quantization_info);
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TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info);
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TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _quantization_info);
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// Create Fully Connected layer info
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FullyConnectedLayerInfo fc_info;
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fc_info.transpose_weights = transpose_weights;
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fc_info.are_weights_reshaped = !reshape_weights;
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fc_info.activation_info = _activation_info;
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// Create and configure function.
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FunctionType fc;
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fc.configure(&src, &weights, &bias, &dst, fc_info);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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weights.allocator()->allocate();
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bias.allocator()->allocate();
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dst.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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fill(AccessorType(src), 0);
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fill(AccessorType(bias), 2);
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if(!reshape_weights || !transpose_weights)
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{
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TensorShape tmp_shape(weights_shape);
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RawTensor tmp(tmp_shape, _data_type, 1);
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// Fill with original shape
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fill(tmp, 1);
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// Transpose elementwise
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tmp = transpose(tmp);
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AccessorType weights_accessor(weights);
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for(int i = 0; i < tmp.num_elements(); ++i)
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{
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Coordinates coord = index2coord(tmp.shape(), i);
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std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)),
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tmp.element_size(),
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static_cast<RawTensor::value_type *>(weights_accessor(coord)));
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}
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}
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else
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{
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fill(AccessorType(weights), 1);
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}
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// Compute NEFullyConnectedLayer function
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fc.run();
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return dst;
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}
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SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape)
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{
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// Create reference
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SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info };
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SimpleTensor<T> weights{ weights_shape, _data_type, 1, _quantization_info };
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SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _quantization_info };
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// Fill reference
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fill(src, 0);
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fill(weights, 1);
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fill(bias, 2);
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return reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, output_shape), _activation_info, _quantization_info);
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}
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TensorType _target{};
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SimpleTensor<T> _reference{};
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DataType _data_type{};
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DataType _bias_data_type{};
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QuantizationInfo _quantization_info{};
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ActivationLayerInfo _activation_info{};
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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template <typename...>
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void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type,
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ActivationLayerInfo activation_info)
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{
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FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
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reshape_weights, data_type,
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QuantizationInfo(), activation_info);
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}
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class FullyConnectedLayerValidationQuantizedFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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template <typename...>
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void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type,
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QuantizationInfo quantization_info, ActivationLayerInfo activation_info)
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{
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FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
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reshape_weights, data_type,
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quantization_info, activation_info);
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}
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};
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */
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