1 /* 2 * Copyright (c) 2018-2021 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24 #ifndef ARM_COMPUTE_TEST_BATCH_TO_SPACE_LAYER_FIXTURE 25 #define ARM_COMPUTE_TEST_BATCH_TO_SPACE_LAYER_FIXTURE 26 27 #include "tests/Globals.h" 28 #include "tests/framework/Asserts.h" 29 #include "tests/framework/Fixture.h" 30 #include "tests/validation/reference/BatchToSpaceLayer.h" 31 32 namespace arm_compute 33 { 34 namespace test 35 { 36 namespace validation 37 { 38 template <typename TensorType, typename AccessorType, typename FunctionType, typename T> 39 class BatchToSpaceLayerValidationFixture : public framework::Fixture 40 { 41 public: 42 template <typename...> setup(TensorShape input_shape,TensorShape block_shape_shape,TensorShape output_shape,DataType data_type,DataLayout data_layout)43 void setup(TensorShape input_shape, TensorShape block_shape_shape, TensorShape output_shape, DataType data_type, DataLayout data_layout) 44 { 45 _target = compute_target(input_shape, block_shape_shape, output_shape, data_type, data_layout); 46 _reference = compute_reference(input_shape, block_shape_shape, output_shape, data_type); 47 } 48 49 protected: 50 template <typename U> fill(U && tensor,int i)51 void fill(U &&tensor, int i) 52 { 53 static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported."); 54 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; 55 56 DistributionType distribution{ T(-1.0f), T(1.0f) }; 57 library->fill(tensor, distribution, i); 58 } compute_target(TensorShape input_shape,TensorShape block_shape_shape,TensorShape output_shape,DataType data_type,DataLayout data_layout)59 TensorType compute_target(TensorShape input_shape, TensorShape block_shape_shape, TensorShape output_shape, 60 DataType data_type, DataLayout data_layout) 61 { 62 if(data_layout == DataLayout::NHWC) 63 { 64 permute(input_shape, PermutationVector(2U, 0U, 1U)); 65 permute(output_shape, PermutationVector(2U, 0U, 1U)); 66 } 67 68 // Create tensors 69 TensorType input = create_tensor<TensorType>(input_shape, data_type, 1, QuantizationInfo(), data_layout); 70 TensorType block_shape = create_tensor<TensorType>(block_shape_shape, DataType::S32); 71 TensorType output = create_tensor<TensorType>(output_shape, data_type, 1, QuantizationInfo(), data_layout); 72 73 // Create and configure function 74 FunctionType batch_to_space; 75 batch_to_space.configure(&input, &block_shape, &output); 76 77 ARM_COMPUTE_ASSERT(input.info()->is_resizable()); 78 ARM_COMPUTE_ASSERT(block_shape.info()->is_resizable()); 79 ARM_COMPUTE_ASSERT(output.info()->is_resizable()); 80 81 // Allocate tensors 82 input.allocator()->allocate(); 83 block_shape.allocator()->allocate(); 84 output.allocator()->allocate(); 85 86 ARM_COMPUTE_ASSERT(!input.info()->is_resizable()); 87 ARM_COMPUTE_ASSERT(!block_shape.info()->is_resizable()); 88 ARM_COMPUTE_ASSERT(!output.info()->is_resizable()); 89 90 // Fill tensors 91 fill(AccessorType(input), 0); 92 { 93 auto block_shape_data = AccessorType(block_shape); 94 const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); 95 for(unsigned int i = 0; i < block_shape_shape.x(); ++i) 96 { 97 static_cast<int32_t *>(block_shape_data.data())[i] = output_shape[i + idx_width] / input_shape[i + idx_width]; 98 } 99 } 100 // Compute function 101 batch_to_space.run(); 102 103 return output; 104 } 105 compute_reference(const TensorShape & input_shape,const TensorShape & block_shape_shape,const TensorShape & output_shape,DataType data_type)106 SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &block_shape_shape, 107 const TensorShape &output_shape, DataType data_type) 108 { 109 // Create reference 110 SimpleTensor<T> input{ input_shape, data_type }; 111 SimpleTensor<int32_t> block_shape{ block_shape_shape, DataType::S32 }; 112 113 // Fill reference 114 fill(input, 0); 115 for(unsigned int i = 0; i < block_shape_shape.x(); ++i) 116 { 117 block_shape[i] = output_shape[i] / input_shape[i]; 118 } 119 120 // Compute reference 121 return reference::batch_to_space(input, block_shape, output_shape); 122 } 123 124 TensorType _target{}; 125 SimpleTensor<T> _reference{}; 126 }; 127 } // namespace validation 128 } // namespace test 129 } // namespace arm_compute 130 #endif /* ARM_COMPUTE_TEST_BATCH_TO_SPACE_LAYER_FIXTURE */ 131