1 /* 2 * Copyright (c) 2022 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_INDIRECT_CONV2D_ADDRESS_PRECALCULATION_FIXTURE 25 #define ARM_COMPUTE_TEST_INDIRECT_CONV2D_ADDRESS_PRECALCULATION_FIXTURE 26 27 #include "arm_compute/core/TensorShape.h" 28 #include "arm_compute/core/Types.h" 29 #include "arm_compute/core/utils/misc/ShapeCalculator.h" 30 #include "tests/Globals.h" 31 #include "tests/framework/Fixture.h" 32 #include "tests/validation/Helpers.h" 33 #include "tests/validation/reference/IndirectConv2dAddressPrecalculation.h" 34 35 namespace arm_compute 36 { 37 namespace test 38 { 39 namespace validation 40 { 41 using namespace arm_compute::misc::shape_calculator; 42 43 template <typename TensorType, typename AccessorType, typename OperatorType> 44 class IndirectConv2dAddressPrecalculationValidationFixture : public framework::Fixture 45 { 46 public: 47 template <typename...> setup(unsigned int src_w,unsigned int src_h,unsigned int src_b,unsigned int wei_w,unsigned int wei_h,unsigned int pad,unsigned int stride,unsigned int m0)48 void setup(unsigned int src_w, 49 unsigned int src_h, 50 unsigned int src_b, 51 unsigned int wei_w, 52 unsigned int wei_h, 53 unsigned int pad, 54 unsigned int stride, 55 unsigned int m0) 56 { 57 DirectConvComputeKernelInfo desc; 58 desc.m0 = m0; 59 desc.n0 = 1; // Not used by the kernel 60 desc.k0 = 1; // Not used by the kernel 61 desc.export_weights_to_cl_image = false; // Not used by the kernel 62 63 const PadStrideInfo conv_info(stride, stride, pad, pad); 64 65 const TensorShape shape_conv_src(23, // The input channels are not used by the kernel 66 src_w, 67 src_h, 68 src_b); 69 70 const TensorShape shape_conv_wei(23, // The input channels are not used by the kernel 71 wei_w, 72 wei_h, 73 23 // The output channels are not used by the kernel 74 ); 75 76 // The result of the kernel does not change with the datatype. Hence, we can fix it to Fp16 for validation purposes 77 const DataType data_type = DataType::F16; 78 79 _target = compute_target(shape_conv_src, shape_conv_wei, data_type, conv_info, desc); 80 _reference = compute_reference(shape_conv_src, shape_conv_wei, data_type, conv_info, desc); 81 } 82 83 protected: compute_target(TensorShape shape_conv_src,TensorShape shape_conv_wei,DataType data_type,const PadStrideInfo & conv_info,const DirectConvComputeKernelInfo & desc)84 TensorType compute_target(TensorShape shape_conv_src, TensorShape shape_conv_wei, DataType data_type, const PadStrideInfo &conv_info, const DirectConvComputeKernelInfo &desc) 85 { 86 TensorInfo src_conv_info(shape_conv_src, 1, data_type, DataLayout::NHWC); 87 TensorInfo wei_conv_info(shape_conv_wei, 1, data_type, DataLayout::NHWC); 88 TensorType dst; 89 90 // The output tensor will be auto-initialized within the function 91 92 // Create and configure function 93 OperatorType func; 94 func.configure(&src_conv_info, &wei_conv_info, dst.info(), conv_info, desc); 95 96 add_padding_x({ &dst }); 97 98 // Allocate tensors 99 dst.allocator()->allocate(); 100 101 // Compute GEMM LHS matrix reshape function 102 ITensorPack tensors = { { ACL_DST, &dst } }; 103 func.run(tensors); 104 105 return dst; 106 } 107 compute_reference(TensorShape shape_conv_src,TensorShape shape_conv_wei,DataType data_type,const PadStrideInfo & conv_info,const DirectConvComputeKernelInfo & desc)108 SimpleTensor<int32_t> compute_reference(TensorShape shape_conv_src, TensorShape shape_conv_wei, DataType data_type, const PadStrideInfo &conv_info, const DirectConvComputeKernelInfo &desc) 109 { 110 ARM_COMPUTE_UNUSED(data_type); 111 TensorShape shape_out = compute_indirect_buffer_shape(shape_conv_src, DataLayout::NHWC, shape_conv_wei, conv_info, desc); 112 TensorShape output_conv_shape = compute_deep_convolution_shape(shape_conv_src, DataLayout::NHWC, shape_conv_wei, conv_info); 113 114 return reference::indirect_conv2d_addr_precalculation(shape_conv_src, shape_conv_wei, output_conv_shape, shape_out, conv_info); 115 } 116 117 TensorType _target{}; 118 SimpleTensor<int32_t> _reference{}; 119 }; 120 } // namespace validation 121 } // namespace test 122 } // namespace arm_compute 123 #endif /* ARM_COMPUTE_TEST_INDIRECT_CONV2D_ADDRESS_PRECALCULATION_FIXTURE */