| /* |
| * Copyright (c) 2017-2018 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 BatchNormalizationLayerValidationFixedPointFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape0, TensorShape shape1, float epsilon, ActivationLayerInfo act_info, DataType dt, int fractional_bits) |
| { |
| _fractional_bits = fractional_bits; |
| _data_type = dt; |
| _target = compute_target(shape0, shape1, epsilon, act_info, dt, fractional_bits); |
| _reference = compute_reference(shape0, shape1, epsilon, act_info, dt, fractional_bits); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&src_tensor, U &&mean_tensor, U &&var_tensor, U &&beta_tensor, U &&gamma_tensor) |
| { |
| if(is_data_type_float(_data_type)) |
| { |
| float min_bound = 0.f; |
| float max_bound = 0.f; |
| std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<T>(); |
| std::uniform_real_distribution<> distribution(min_bound, max_bound); |
| std::uniform_real_distribution<> distribution_var(0, max_bound); |
| library->fill(src_tensor, distribution, 0); |
| library->fill(mean_tensor, distribution, 1); |
| library->fill(var_tensor, distribution_var, 0); |
| library->fill(beta_tensor, distribution, 3); |
| library->fill(gamma_tensor, distribution, 4); |
| } |
| else |
| { |
| int min_bound = 0; |
| int max_bound = 0; |
| std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<T>(_fractional_bits); |
| std::uniform_int_distribution<> distribution(min_bound, max_bound); |
| std::uniform_int_distribution<> distribution_var(0, max_bound); |
| library->fill(src_tensor, distribution, 0); |
| library->fill(mean_tensor, distribution, 1); |
| library->fill(var_tensor, distribution_var, 0); |
| library->fill(beta_tensor, distribution, 3); |
| library->fill(gamma_tensor, distribution, 4); |
| } |
| } |
| |
| TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, float epsilon, ActivationLayerInfo act_info, DataType dt, int fixed_point_position) |
| { |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(shape0, dt, 1, fixed_point_position); |
| TensorType dst = create_tensor<TensorType>(shape0, dt, 1, fixed_point_position); |
| TensorType mean = create_tensor<TensorType>(shape1, dt, 1, fixed_point_position); |
| TensorType var = create_tensor<TensorType>(shape1, dt, 1, fixed_point_position); |
| TensorType beta = create_tensor<TensorType>(shape1, dt, 1, fixed_point_position); |
| TensorType gamma = create_tensor<TensorType>(shape1, dt, 1, fixed_point_position); |
| |
| // Create and configure function |
| FunctionType norm; |
| norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon, act_info); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(mean.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(var.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(beta.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(gamma.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| mean.allocator()->allocate(); |
| var.allocator()->allocate(); |
| beta.allocator()->allocate(); |
| gamma.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!mean.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!var.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!beta.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!gamma.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // 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, int fixed_point_position) |
| { |
| // Create reference |
| SimpleTensor<T> ref_src{ shape0, dt, 1, fixed_point_position }; |
| SimpleTensor<T> ref_mean{ shape1, dt, 1, fixed_point_position }; |
| SimpleTensor<T> ref_var{ shape1, dt, 1, fixed_point_position }; |
| SimpleTensor<T> ref_beta{ shape1, dt, 1, fixed_point_position }; |
| SimpleTensor<T> ref_gamma{ shape1, dt, 1, fixed_point_position }; |
| |
| // 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, fixed_point_position); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| int _fractional_bits{}; |
| DataType _data_type{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class BatchNormalizationLayerValidationFixture : public BatchNormalizationLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape0, TensorShape shape1, float epsilon, ActivationLayerInfo act_info, DataType dt) |
| { |
| BatchNormalizationLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(shape0, shape1, epsilon, act_info, dt, 0); |
| } |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FIXTURE */ |