166 lines
6.8 KiB
C++
166 lines
6.8 KiB
C++
/*
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* Copyright (c) 2017-2018 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_BATCH_NORMALIZATION_LAYER_FIXTURE
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#define ARM_COMPUTE_TEST_BATCH_NORMALIZATION_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 "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/BatchNormalizationLayer.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 BatchNormalizationLayerValidationFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(TensorShape shape0, TensorShape shape1, float epsilon, bool use_beta, bool use_gamma, ActivationLayerInfo act_info, DataType dt, DataLayout data_layout)
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{
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_data_type = dt;
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_use_beta = use_beta;
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_use_gamma = use_gamma;
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_target = compute_target(shape0, shape1, epsilon, act_info, dt, data_layout);
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_reference = compute_reference(shape0, shape1, epsilon, act_info, dt);
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}
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protected:
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template <typename U>
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void fill(U &&src_tensor, U &&mean_tensor, U &&var_tensor, U &&beta_tensor, U &&gamma_tensor)
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{
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const float min_bound = -1.f;
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const float max_bound = 1.f;
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std::uniform_real_distribution<> distribution(min_bound, max_bound);
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std::uniform_real_distribution<> distribution_var(0, max_bound);
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library->fill(src_tensor, distribution, 0);
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library->fill(mean_tensor, distribution, 1);
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library->fill(var_tensor, distribution_var, 0);
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if(_use_beta)
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{
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library->fill(beta_tensor, distribution, 3);
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}
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else
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{
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// Fill with default value 0.f
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library->fill_tensor_value(beta_tensor, 0.f);
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}
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if(_use_gamma)
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{
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library->fill(gamma_tensor, distribution, 4);
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}
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else
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{
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// Fill with default value 1.f
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library->fill_tensor_value(gamma_tensor, 1.f);
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}
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}
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TensorType compute_target(TensorShape shape0, const TensorShape &shape1, float epsilon, ActivationLayerInfo act_info, DataType dt, DataLayout data_layout)
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{
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if(data_layout == DataLayout::NHWC)
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{
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permute(shape0, PermutationVector(2U, 0U, 1U));
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}
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// Create tensors
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TensorType src = create_tensor<TensorType>(shape0, dt, 1, QuantizationInfo(), data_layout);
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TensorType dst = create_tensor<TensorType>(shape0, dt, 1, QuantizationInfo(), data_layout);
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TensorType mean = create_tensor<TensorType>(shape1, dt, 1);
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TensorType var = create_tensor<TensorType>(shape1, dt, 1);
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TensorType beta = create_tensor<TensorType>(shape1, dt, 1);
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TensorType gamma = create_tensor<TensorType>(shape1, dt, 1);
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// Create and configure function
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FunctionType norm;
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TensorType *beta_ptr = _use_beta ? &beta : nullptr;
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TensorType *gamma_ptr = _use_gamma ? &gamma : nullptr;
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norm.configure(&src, &dst, &mean, &var, beta_ptr, gamma_ptr, epsilon, act_info);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(mean.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(var.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(beta.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(gamma.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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mean.allocator()->allocate();
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var.allocator()->allocate();
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beta.allocator()->allocate();
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gamma.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!mean.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!var.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!beta.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!gamma.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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fill(AccessorType(src), AccessorType(mean), AccessorType(var), AccessorType(beta), AccessorType(gamma));
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// Compute function
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norm.run();
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return dst;
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}
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SimpleTensor<T> compute_reference(const TensorShape &shape0, const TensorShape &shape1, float epsilon, ActivationLayerInfo act_info, DataType dt)
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{
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// Create reference
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SimpleTensor<T> ref_src{ shape0, dt, 1 };
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SimpleTensor<T> ref_mean{ shape1, dt, 1 };
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SimpleTensor<T> ref_var{ shape1, dt, 1 };
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SimpleTensor<T> ref_beta{ shape1, dt, 1 };
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SimpleTensor<T> ref_gamma{ shape1, dt, 1 };
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// Fill reference
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fill(ref_src, ref_mean, ref_var, ref_beta, ref_gamma);
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return reference::batch_normalization_layer(ref_src, ref_mean, ref_var, ref_beta, ref_gamma, epsilon, act_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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bool _use_beta{};
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bool _use_gamma{};
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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_BATCH_NORMALIZATION_LAYER_FIXTURE */
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