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269 lines
12 KiB
269 lines
12 KiB
/*
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* Copyright (c) 2023 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 TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE
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#define TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE
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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 "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/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/ArithmeticOperations.h"
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#include "tests/validation/reference/DequantizationLayer.h"
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#include "tests/validation/reference/PixelWiseMultiplication.h"
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#include "tests/validation/reference/QuantizationLayer.h"
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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 AddMulAddGenericFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info, bool interm_out)
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{
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compute_target(shape, data_type, act_info, interm_out);
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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, DataType data_type)
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{
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switch(data_type)
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{
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case DataType::F32:
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library->fill_tensor_uniform(tensor, i, -10.f, 10.f);
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break;
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case DataType::F16:
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library->fill_tensor_uniform(tensor, i, -1.f, 1.f);
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break;
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default:
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library->fill_tensor_uniform(tensor, i);
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break;
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}
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}
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void compute_target(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info, bool interm_out)
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{
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TensorShape b_shape(shape.x());
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// Create tensors
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TensorType input1 = create_tensor<TensorType>(shape, data_type, 1, _input1_qinfo);
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TensorType input2 = create_tensor<TensorType>(shape, data_type, 1, _input2_qinfo);
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TensorType bn_mul = create_tensor<TensorType>(b_shape, data_type, 1, _bn_mul_qinfo);
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TensorType bn_add = create_tensor<TensorType>(b_shape, data_type, 1, _bn_add_qinfo);
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TensorType add_output = create_tensor<TensorType>(shape, data_type, 1, _add_output_qinfo);
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TensorType final_output = create_tensor<TensorType>(shape, data_type, 1, _final_output_qinfo);
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// Create and configure function
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FunctionType add_mul_add;
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add_mul_add.configure(&input1, &input2, &bn_mul, &bn_add, interm_out ? &add_output : nullptr, &final_output, ConvertPolicy::SATURATE, act_info);
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// Allocate tensors
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input1.allocator()->allocate();
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input2.allocator()->allocate();
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bn_mul.allocator()->allocate();
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bn_add.allocator()->allocate();
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if(interm_out)
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{
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add_output.allocator()->allocate();
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}
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final_output.allocator()->allocate();
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// Fill tensors
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fill(AccessorType(input1), 0, data_type);
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fill(AccessorType(input2), 1, data_type);
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fill(AccessorType(bn_mul), 2, data_type);
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fill(AccessorType(bn_add), 3, data_type);
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// // Compute function
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add_mul_add.run();
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_target = std::move(final_output);
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if(interm_out)
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{
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_interm_target = std::move(add_output);
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}
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}
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TensorType _target{};
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TensorType _interm_target{};
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SimpleTensor<T> _reference{};
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SimpleTensor<T> _interm_reference{};
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QuantizationInfo _input1_qinfo{};
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QuantizationInfo _input2_qinfo{};
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QuantizationInfo _bn_mul_qinfo{};
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QuantizationInfo _bn_add_qinfo{};
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QuantizationInfo _add_output_qinfo{};
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QuantizationInfo _final_output_qinfo{};
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool interm_out>
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class AddMulAddFloatValidationFixture : public AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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using Parent = AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T>;
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template <typename...>
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void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo act_info)
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{
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Parent::setup(shape, data_type, act_info, interm_out);
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compute_reference(shape, data_type, act_info);
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}
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// Compute Reference is moved outside of the generic fixture because with the quantized data types,
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// it becomes a very different implementation with intermediate tensors' data types being always float.
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// This way the reference calculations are more readable and the size of the classes will be smaller
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// due to unrepeated fill() and target() methods.
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void compute_reference(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info)
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{
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TensorShape b_shape(shape.x());
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// Create reference
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SimpleTensor<T> input1{ shape, data_type };
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SimpleTensor<T> input2{ shape, data_type };
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SimpleTensor<T> bn_mul{ b_shape, data_type };
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SimpleTensor<T> bn_add{ b_shape, data_type };
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SimpleTensor<T> add_output{ shape, data_type, 1 };
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SimpleTensor<T> bn_mul_out{ shape, data_type };
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SimpleTensor<T> bn_add_out{ shape, data_type };
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// Fill reference
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Parent::fill(input1, 0, data_type);
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Parent::fill(input2, 1, data_type);
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Parent::fill(bn_mul, 2, data_type);
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Parent::fill(bn_add, 3, data_type);
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reference::arithmetic_operation<T>(reference::ArithmeticOperation::ADD, input1, input2, add_output, ConvertPolicy::SATURATE);
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bn_mul_out = reference::pixel_wise_multiplication<T, T, T>(add_output, bn_mul, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP, data_type);
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reference::arithmetic_operation<T>(reference::ArithmeticOperation::ADD, bn_mul_out, bn_add, bn_add_out, ConvertPolicy::SATURATE);
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if(interm_out)
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{
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Parent::_interm_reference = std::move(add_output);
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}
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if(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::IDENTITY)
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{
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Parent::_reference = reference::activation_layer(bn_add_out, act_info);
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}
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else
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{
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Parent::_reference = std::move(bn_add_out);
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}
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}
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool interm_out>
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class AddMulAddQuantizedValidationFixture : public AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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using Parent = AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T>;
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template <typename...>
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void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo act_info,
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QuantizationInfo input1_qinfo, QuantizationInfo input2_qinfo, QuantizationInfo bn_mul_qinfo,
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QuantizationInfo bn_add_qinfo, QuantizationInfo add_output_qinfo, QuantizationInfo final_output_qinfo)
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{
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// Quantization arguments moved to class attributes to prevent long function declerations
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Parent::_input1_qinfo = input1_qinfo;
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Parent::_input2_qinfo = input2_qinfo;
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Parent::_bn_mul_qinfo = bn_mul_qinfo;
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Parent::_bn_add_qinfo = bn_add_qinfo;
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Parent::_add_output_qinfo = add_output_qinfo;
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Parent::_final_output_qinfo = final_output_qinfo;
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Parent::setup(shape, data_type, act_info, interm_out);
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compute_reference(shape, data_type, act_info);
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}
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// Compute Reference is moved outside of the generic fixture because with the quantized data types,
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// it becomes a very different implementation with intermediate tensors' data types being always float.
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// This way the reference calculations are more readable and the size of the classes will be smaller
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// due to unrepeated fill() and target() methods.
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void compute_reference(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info)
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{
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TensorShape b_shape(shape.x());
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// Create reference
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SimpleTensor<T> input1{ shape, data_type, 1, Parent::_input1_qinfo };
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SimpleTensor<T> input2{ shape, data_type, 1, Parent::_input2_qinfo };
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SimpleTensor<T> bn_mul{ b_shape, data_type, 1, Parent::_bn_mul_qinfo };
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SimpleTensor<T> bn_add{ b_shape, data_type, 1, Parent::_bn_add_qinfo };
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// Fill input tensors
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Parent::fill(input1, 0, data_type);
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Parent::fill(input2, 1, data_type);
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Parent::fill(bn_mul, 2, data_type);
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Parent::fill(bn_add, 3, data_type);
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SimpleTensor<float> input1_dequantized = reference::dequantization_layer<float>(input1);
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SimpleTensor<float> input2_dequantized = reference::dequantization_layer<float>(input2);
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SimpleTensor<float> bn_mul_dequantized = reference::dequantization_layer<float>(bn_mul);
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SimpleTensor<float> bn_add_dequantized = reference::dequantization_layer<float>(bn_add);
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SimpleTensor<float> add_output_dequantized{ shape, DataType::F32 };
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SimpleTensor<float> bn_add_out_dequantized{ shape, DataType::F32 };
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reference::arithmetic_operation<float>(reference::ArithmeticOperation::ADD, input1_dequantized, input2_dequantized, add_output_dequantized, ConvertPolicy::SATURATE);
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SimpleTensor<float> bn_mul_out_dequantized = reference::pixel_wise_multiplication<float, float, float>(add_output_dequantized, bn_mul_dequantized, 1.f, ConvertPolicy::SATURATE,
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RoundingPolicy::TO_NEAREST_UP, DataType::F32);
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reference::arithmetic_operation<float>(reference::ArithmeticOperation::ADD, bn_mul_out_dequantized, bn_add_dequantized, bn_add_out_dequantized, ConvertPolicy::SATURATE);
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if(interm_out)
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{
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Parent::_interm_reference = reference::quantization_layer<float, T>(add_output_dequantized, data_type, Parent::_add_output_qinfo);
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}
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if(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::IDENTITY)
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{
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SimpleTensor<T> ref = reference::quantization_layer<float, T>(bn_add_out_dequantized, data_type, Parent::_final_output_qinfo);
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Parent::_reference = reference::activation_layer(ref, act_info);
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}
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else
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{
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Parent::_reference = reference::quantization_layer<float, T>(bn_add_out_dequantized, data_type, Parent::_final_output_qinfo);
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}
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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 /* TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE */
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