| /* |
| * Copyright (c) 2017-2021 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_BATCH_NORMALIZATION_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "tests/AssetsLibrary.h" |
| #include "tests/Globals.h" |
| #include "tests/IAccessor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/reference/BatchNormalizationLayer.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class BatchNormalizationLayerValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape0, TensorShape shape1, float epsilon, bool use_beta, bool use_gamma, ActivationLayerInfo act_info, DataType dt, DataLayout data_layout) |
| { |
| _data_type = dt; |
| _use_beta = use_beta; |
| _use_gamma = use_gamma; |
| |
| _target = compute_target(shape0, shape1, epsilon, act_info, dt, data_layout); |
| _reference = compute_reference(shape0, shape1, epsilon, act_info, dt); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&src_tensor, U &&mean_tensor, U &&var_tensor, U &&beta_tensor, U &&gamma_tensor) |
| { |
| static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported."); |
| using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type; |
| |
| const T min_bound = T(-1.f); |
| const T max_bound = T(1.f); |
| DistributionType distribution{ min_bound, max_bound }; |
| DistributionType distribution_var{ T(0.f), max_bound }; |
| |
| library->fill(src_tensor, distribution, 0); |
| library->fill(mean_tensor, distribution, 1); |
| library->fill(var_tensor, distribution_var, 0); |
| if(_use_beta) |
| { |
| library->fill(beta_tensor, distribution, 3); |
| } |
| else |
| { |
| // Fill with default value 0.f |
| library->fill_tensor_value(beta_tensor, T(0.f)); |
| } |
| if(_use_gamma) |
| { |
| library->fill(gamma_tensor, distribution, 4); |
| } |
| else |
| { |
| // Fill with default value 1.f |
| library->fill_tensor_value(gamma_tensor, T(1.f)); |
| } |
| } |
| |
| TensorType compute_target(TensorShape shape0, const TensorShape &shape1, float epsilon, ActivationLayerInfo act_info, DataType dt, DataLayout data_layout) |
| { |
| if(data_layout == DataLayout::NHWC) |
| { |
| permute(shape0, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(shape0, dt, 1, QuantizationInfo(), data_layout); |
| TensorType dst = create_tensor<TensorType>(shape0, dt, 1, QuantizationInfo(), data_layout); |
| TensorType mean = create_tensor<TensorType>(shape1, dt, 1); |
| TensorType var = create_tensor<TensorType>(shape1, dt, 1); |
| TensorType beta = create_tensor<TensorType>(shape1, dt, 1); |
| TensorType gamma = create_tensor<TensorType>(shape1, dt, 1); |
| |
| // Create and configure function |
| FunctionType norm; |
| TensorType *beta_ptr = _use_beta ? &beta : nullptr; |
| TensorType *gamma_ptr = _use_gamma ? &gamma : nullptr; |
| norm.configure(&src, &dst, &mean, &var, beta_ptr, gamma_ptr, epsilon, act_info); |
| |
| ARM_COMPUTE_ASSERT(src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(mean.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(var.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(beta.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(gamma.info()->is_resizable()); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| mean.allocator()->allocate(); |
| var.allocator()->allocate(); |
| beta.allocator()->allocate(); |
| gamma.allocator()->allocate(); |
| |
| ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!mean.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!var.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!beta.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!gamma.info()->is_resizable()); |
| |
| // Fill tensors |
| fill(AccessorType(src), AccessorType(mean), AccessorType(var), AccessorType(beta), AccessorType(gamma)); |
| |
| // Compute function |
| norm.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference(const TensorShape &shape0, const TensorShape &shape1, float epsilon, ActivationLayerInfo act_info, DataType dt) |
| { |
| // Create reference |
| SimpleTensor<T> ref_src{ shape0, dt, 1 }; |
| SimpleTensor<T> ref_mean{ shape1, dt, 1 }; |
| SimpleTensor<T> ref_var{ shape1, dt, 1 }; |
| SimpleTensor<T> ref_beta{ shape1, dt, 1 }; |
| SimpleTensor<T> ref_gamma{ shape1, dt, 1 }; |
| |
| // Fill reference |
| fill(ref_src, ref_mean, ref_var, ref_beta, ref_gamma); |
| |
| return reference::batch_normalization_layer(ref_src, ref_mean, ref_var, ref_beta, ref_gamma, epsilon, act_info); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| DataType _data_type{}; |
| bool _use_beta{}; |
| bool _use_gamma{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FIXTURE */ |