text: /*
   * Copyright (c) 2019 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_FUSEBATCHNORMALIZATION_FIXTURE
  #define ARM_COMPUTE_TEST_FUSEBATCHNORMALIZATION_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/FuseBatchNormalization.h"
  
  #include <tuple>
  #include <utility>
  
  namespace arm_compute
  {
  namespace test
  {
  namespace validation
  {
  template <typename TensorType, typename AccessorType, typename FunctionType, int dims_weights, typename T>
  class FuseBatchNormalizationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(TensorShape shape_w, DataType data_type, DataLayout data_layout, bool in_place, bool with_bias, bool with_gamma, bool with_beta)
      {
          std::tie(_target_w, _target_b)       = compute_target(shape_w, data_type, data_layout, in_place, with_bias, with_gamma, with_beta);
          std::tie(_reference_w, _reference_b) = compute_reference(shape_w, data_type, with_bias, with_gamma, with_beta);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i, float min, float max)
      {
          library->fill_tensor_uniform(tensor, i, min, max);
      }
  
      std::pair<TensorType, TensorType> compute_target(TensorShape shape_w, DataType data_type, DataLayout data_layout, bool in_place, bool with_bias, bool with_gamma, bool with_beta)
      {
          const TensorShape shape_v(shape_w[dims_weights - 1]);
  
          if(data_layout == DataLayout::NHWC)
          {
              permute(shape_w, PermutationVector(2U, 0U, 1U));
          }
  
          const bool in_place_w = in_place;
          const bool in_place_b = with_bias ? in_place : false;
  
          // Create tensors
          TensorType w       = create_tensor<TensorType>(shape_w, data_type, 1, QuantizationInfo(), data_layout);
          TensorType b       = create_tensor<TensorType>(shape_v, data_type);
          TensorType mean    = create_tensor<TensorType>(shape_v, data_type);
          TensorType var     = create_tensor<TensorType>(shape_v, data_type);
          TensorType w_fused = create_tensor<TensorType>(shape_w, data_type, 1, QuantizationInfo(), data_layout);
          TensorType b_fused = create_tensor<TensorType>(shape_v, data_type);
          TensorType beta    = create_tensor<TensorType>(shape_v, data_type);
          TensorType gamma   = create_tensor<TensorType>(shape_v, data_type);
  
          auto b_to_use       = with_bias ? &b : nullptr;
          auto gamma_to_use   = with_gamma ? &gamma : nullptr;
          auto beta_to_use    = with_beta ? &beta : nullptr;
          auto w_fused_to_use = in_place_w ? nullptr : &w_fused;
          auto b_fused_to_use = in_place_b ? nullptr : &b_fused;
  
          const FuseBatchNormalizationType fuse_bn_type = dims_weights == 3 ?
                                                          FuseBatchNormalizationType::DEPTHWISECONVOLUTION :
                                                          FuseBatchNormalizationType::CONVOLUTION;
          // Create and configure function
          FunctionType fuse_batch_normalization;
          fuse_batch_normalization.configure(&w, &mean, &var, w_fused_to_use, b_fused_to_use, b_to_use, beta_to_use, gamma_to_use, _epsilon, fuse_bn_type);
  
          ARM_COMPUTE_EXPECT(w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(b.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(w_fused.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(b_fused.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
          w.allocator()->allocate();
          b.allocator()->allocate();
          mean.allocator()->allocate();
          var.allocator()->allocate();
          w_fused.allocator()->allocate();
          b_fused.allocator()->allocate();
          beta.allocator()->allocate();
          gamma.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!b.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(!w_fused.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!b_fused.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(w), 0U, -1.0f, 1.0f);
          fill(AccessorType(b), 1U, -1.0f, 1.0f);
          fill(AccessorType(mean), 2U, -1.0f, 1.0f);
          fill(AccessorType(var), 3U, 0.0f, 1.0f);
          fill(AccessorType(beta), 4U, -1.0f, 1.0f);
          fill(AccessorType(gamma), 5U, -1.0f, 1.0f);
  
          // Compute function
          fuse_batch_normalization.run();
  
          return std::make_pair(std::move(in_place_w ? w : w_fused), std::move(in_place_b ? b : b_fused));
      }
  
      std::pair<SimpleTensor<T>, SimpleTensor<T>> compute_reference(TensorShape shape_w, DataType data_type, bool with_bias, bool with_gamma, bool with_beta)
      {
          const TensorShape shape_v(shape_w[dims_weights - 1]);
  
          SimpleTensor<T> w{ shape_w, data_type };
          SimpleTensor<T> b{ shape_v, data_type };
          SimpleTensor<T> mean{ shape_v, data_type };
          SimpleTensor<T> var{ shape_v, data_type };
          SimpleTensor<T> w_fused{ shape_w, data_type };
          SimpleTensor<T> b_fused{ shape_v, data_type };
          SimpleTensor<T> beta{ shape_v, data_type };
          SimpleTensor<T> gamma{ shape_v, data_type };
  
          // Fill reference tensor
          fill(w, 0U, -1.0f, 1.0f);
          fill(b, 1U, -1.0f, 1.0f);
          fill(mean, 2U, -1.0f, 1.0f);
          fill(var, 3U, 0.0f, 1.0f);
          fill(beta, 4U, -1.0f, 1.0f);
          fill(gamma, 5U, -1.0f, 1.0f);
  
          if(!with_bias)
          {
              // Fill with zeros
              fill(b, 0U, 0.0f, 0.0f);
          }
  
          if(!with_gamma)
          {
              // Fill with ones
              fill(gamma, 0U, 1.0f, 1.0f);
          }
  
          if(!with_beta)
          {
              // Fill with zeros
              fill(beta, 0U, 0.0f, 0.0f);
          }
  
          switch(dims_weights)
          {
              case 3:
                  // Weights for depth wise convolution layer
                  reference::fuse_batch_normalization_dwc_layer(w, mean, var, w_fused, b_fused, b, beta, gamma, _epsilon);
                  break;
              case 4:
                  // Weights for convolution layer
                  reference::fuse_batch_normalization_conv_layer(w, mean, var, w_fused, b_fused, b, beta, gamma, _epsilon);
                  break;
              default:
                  ARM_COMPUTE_ERROR("Not supported number of dimensions for the input weights tensor");
          }
  
          return std::make_pair(std::move(w_fused), std::move(b_fused));
      }
  
      const float     _epsilon{ 0.0001f };
      TensorType      _target_w{};
      TensorType      _target_b{};
      SimpleTensor<T> _reference_w{};
      SimpleTensor<T> _reference_b{};
  };
  } // namespace validation
  } // namespace test
  } // namespace arm_compute
  #endif /* ARM_COMPUTE_TEST_FUSEBATCHNORMALIZATION_FIXTURE */
  