Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 1 | /* |
Gunes Bayir | 3a1e125 | 2023-01-03 21:26:09 +0000 | [diff] [blame] | 2 | * Copyright (c) 2022-2023 Arm Limited. |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 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 | */ |
Ramy Elgammal | 404462a | 2022-11-08 02:14:46 +0000 | [diff] [blame] | 24 | #ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE |
| 25 | #define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 26 | |
| 27 | #include "arm_compute/core/CL/CLKernelLibrary.h" |
| 28 | #include "arm_compute/core/TensorInfo.h" |
| 29 | #include "arm_compute/core/Types.h" |
| 30 | |
SiCong Li | 5a63d1e | 2023-01-06 16:28:57 +0000 | [diff] [blame] | 31 | #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 32 | #include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h" |
SiCong Li | 5a63d1e | 2023-01-06 16:28:57 +0000 | [diff] [blame] | 33 | #include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h" |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 34 | #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h" |
| 35 | #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h" |
Viet-Hoa Do | b84e253 | 2022-12-13 13:09:10 +0000 | [diff] [blame] | 36 | #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h" |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 37 | |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 38 | #include "tests/CL/CLAccessor.h" |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 39 | #include "tests/framework/Fixture.h" |
| 40 | #include "tests/framework/Macros.h" |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 41 | #include "tests/validation/Validation.h" |
| 42 | #include "tests/validation/reference/ConvolutionLayer.h" |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 43 | #include "tests/validation/reference/Permute.h" |
| 44 | |
| 45 | using namespace arm_compute::experimental::dynamic_fusion; |
| 46 | |
| 47 | namespace arm_compute |
| 48 | { |
| 49 | namespace test |
| 50 | { |
| 51 | namespace validation |
| 52 | { |
SiCong Li | 5a63d1e | 2023-01-06 16:28:57 +0000 | [diff] [blame] | 53 | namespace |
| 54 | { |
| 55 | template <typename U> |
| 56 | void fill(U &&tensor, int i) |
| 57 | { |
| 58 | switch(tensor.data_type()) |
| 59 | { |
| 60 | case DataType::F16: |
| 61 | { |
| 62 | arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f }; |
| 63 | library->fill(tensor, distribution, i); |
| 64 | break; |
| 65 | } |
| 66 | case DataType::F32: |
| 67 | { |
| 68 | std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); |
| 69 | library->fill(tensor, distribution, i); |
| 70 | break; |
| 71 | } |
| 72 | default: |
| 73 | library->fill_tensor_uniform(tensor, i); |
| 74 | } |
| 75 | } |
| 76 | |
| 77 | } // namespace |
| 78 | |
| 79 | /** General Conv2d fixture |
| 80 | * Adapted from tests/validation/fixtures/ConvolutionLayerFixture.h |
| 81 | * TODO: Parameterize to be fully backend agnostic: COMPMID-5760; remove Gpu from name |
| 82 | */ |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 83 | template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| 84 | class DynamicFusionGpuConv2dValidationGenericFixture : public framework::Fixture |
| 85 | { |
| 86 | public: |
| 87 | using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value |
| 88 | || std::is_same<typename std::decay<T>::type, int8_t>::value, |
| 89 | int32_t, T >::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T |
| 90 | |
| 91 | template <typename...> |
| 92 | void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, const PadStrideInfo &info, const Size2D &dilation, DataType data_type, |
| 93 | DataLayout data_layout, QuantizationInfo quantization_info, QuantizationInfo weight_quantization_info) |
| 94 | { |
| 95 | ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout |
| 96 | const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, dilation); |
| 97 | _data_type = data_type; |
| 98 | _data_layout = data_layout; |
| 99 | _is_quantized = is_data_type_quantized_asymmetric(data_type); |
| 100 | _quantization_info = quantization_info; |
| 101 | _weight_quantization_info = weight_quantization_info; |
| 102 | _bias_data_type = _is_quantized ? DataType::S32 : data_type; |
| 103 | _target = compute_target(input_shape, weights_shape, bias_shape, conv2d_attr); |
| 104 | _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr); |
| 105 | } |
| 106 | |
| 107 | protected: |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 108 | // Given input is in nchw format |
| 109 | TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, Conv2dAttributes conv2d_attr) |
| 110 | { |
| 111 | ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC); |
| 112 | permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| 113 | permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| 114 | CLScheduler::get().default_reinit(); |
| 115 | |
| 116 | // Create a new workload sketch |
| 117 | auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); |
| 118 | auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx }; |
| 119 | GpuWorkloadSketch sketch{ &gpu_ctx }; |
| 120 | |
| 121 | // Create sketch tensors |
Gunes Bayir | 3a1e125 | 2023-01-03 21:26:09 +0000 | [diff] [blame] | 122 | TensorInfo input_info = sketch.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout)); |
| 123 | TensorInfo weight_info = sketch.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout)); |
| 124 | TensorInfo bias_info = sketch.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout)); |
| 125 | TensorInfo dst_info = sketch.create_tensor_info(); |
Viet-Hoa Do | b84e253 | 2022-12-13 13:09:10 +0000 | [diff] [blame] | 126 | |
Gunes Bayir | 3a1e125 | 2023-01-03 21:26:09 +0000 | [diff] [blame] | 127 | ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr); |
| 128 | GpuOutput::create_op(sketch, ans_info, &dst_info); |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 129 | |
| 130 | // Configure runtime |
| 131 | ClWorkloadRuntime runtime; |
| 132 | runtime.configure(sketch); |
| 133 | // (Important) Allocate auxiliary tensor memory if there are any |
| 134 | for(auto &data : runtime.get_auxiliary_tensors()) |
| 135 | { |
Ramy Elgammal | 002e653 | 2023-01-11 18:48:04 +0000 | [diff] [blame] | 136 | CLTensor *tensor = std::get<0>(data); |
| 137 | TensorInfo info = std::get<1>(data); |
| 138 | AuxMemoryInfo aux_mem_req = std::get<2>(data); |
| 139 | tensor->allocator()->init(info, aux_mem_req.alignment); |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 140 | tensor->allocator()->allocate(); // Use ACL allocated memory |
| 141 | } |
| 142 | // Construct user tensors |
Ramy Elgammal | 404462a | 2022-11-08 02:14:46 +0000 | [diff] [blame] | 143 | TensorType t_input{}; |
| 144 | TensorType t_weight{}; |
| 145 | TensorType t_bias{}; |
| 146 | TensorType t_dst{}; |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 147 | |
| 148 | // Initialize user tensors |
| 149 | t_input.allocator()->init(input_info); |
| 150 | t_weight.allocator()->init(weight_info); |
| 151 | t_bias.allocator()->init(bias_info); |
| 152 | t_dst.allocator()->init(dst_info); |
| 153 | |
| 154 | // Allocate and fill user tensors |
| 155 | t_input.allocator()->allocate(); |
| 156 | t_weight.allocator()->allocate(); |
| 157 | t_bias.allocator()->allocate(); |
| 158 | t_dst.allocator()->allocate(); |
Ramy Elgammal | 404462a | 2022-11-08 02:14:46 +0000 | [diff] [blame] | 159 | |
| 160 | fill(AccessorType(t_input), 0); |
| 161 | fill(AccessorType(t_weight), 1); |
| 162 | fill(AccessorType(t_bias), 2); |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 163 | |
| 164 | // Run runtime |
| 165 | runtime.run({ &t_input, &t_weight, &t_bias, &t_dst }); |
| 166 | return t_dst; |
| 167 | } |
| 168 | |
| 169 | SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, |
| 170 | const TensorShape &output_shape, Conv2dAttributes conv2d_attr) |
| 171 | { |
| 172 | // Create reference |
| 173 | SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info }; |
| 174 | SimpleTensor<T> weight{ weights_shape, _data_type, 1, _weight_quantization_info }; |
| 175 | SimpleTensor<TBias> bias{ bias_shape, _data_type, 1, _quantization_info }; |
| 176 | |
| 177 | fill(src, 0); |
| 178 | fill(weight, 1); |
| 179 | fill(bias, 2); |
| 180 | |
| 181 | auto src_nchw = src; |
| 182 | auto weights_nchw = weight; |
| 183 | auto bias_nchw = bias; |
| 184 | auto output_shape_nchw = output_shape; |
| 185 | |
| 186 | PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom, |
| 187 | DimensionRoundingType{}); |
| 188 | auto dst_nchw = reference::convolution_layer(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride, conv2d_attr.dilation()); |
| 189 | return dst_nchw; |
| 190 | } |
| 191 | |
| 192 | TensorType _target{}; |
| 193 | SimpleTensor<T> _reference{}; |
| 194 | DataType _data_type{}; |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 195 | DataType _bias_data_type{}; |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 196 | DataLayout _data_layout{}; |
| 197 | QuantizationInfo _quantization_info{}; |
| 198 | QuantizationInfo _weight_quantization_info{}; |
| 199 | bool _is_quantized = false; |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 200 | }; |
| 201 | |
| 202 | template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| 203 | class DynamicFusionGpuConv2dValidationFixture : public DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| 204 | { |
| 205 | public: |
| 206 | template <typename...> |
| 207 | void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape output_shape, TensorShape bias_shape, |
| 208 | const PadStrideInfo &info, const Size2D &dialation, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info) |
| 209 | { |
| 210 | DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, output_shape, bias_shape, info, dialation, |
Ramy Elgammal | 404462a | 2022-11-08 02:14:46 +0000 | [diff] [blame] | 211 | data_type, data_layout, quantization_info, quantization_info); |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 212 | } |
| 213 | }; |
SiCong Li | 5a63d1e | 2023-01-06 16:28:57 +0000 | [diff] [blame] | 214 | |
| 215 | /** Specific Conv2d method: Direct Conv2d fixture |
| 216 | * Adapted from tests/validation/fixtures/DirectConvolutionLayerFixture.h |
| 217 | * TODO: Parameterize to be fully backend agnostic: COMPMID-5760 |
| 218 | */ |
| 219 | template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| 220 | class DynamicFusionDirectConv2dValidationGenericFixture : public framework::Fixture |
| 221 | { |
| 222 | public: |
| 223 | using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type; |
| 224 | |
| 225 | template <typename...> |
| 226 | void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, |
| 227 | DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout) |
| 228 | { |
| 229 | ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout |
| 230 | |
| 231 | TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels); |
| 232 | const TensorShape bias_shape(num_kernels); |
| 233 | const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR); |
| 234 | const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| 235 | |
| 236 | const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, { 1U, 1U } /* dilation */); |
| 237 | |
| 238 | TensorInfo input_info = TensorInfo(input_shape, 1, data_type); |
| 239 | TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type); |
| 240 | |
| 241 | const TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_info, weights_info, info); |
| 242 | |
| 243 | _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr, data_type, bias_data_type, quantization_info, data_layout); |
| 244 | _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info); |
| 245 | } |
| 246 | |
| 247 | protected: |
| 248 | TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const Conv2dAttributes &conv2d_attr, |
| 249 | DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info, const DataLayout &data_layout) |
| 250 | { |
| 251 | ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); |
| 252 | ARM_COMPUTE_UNUSED(quantization_info); |
| 253 | // Dataset shapes are in NCHW layout |
| 254 | permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| 255 | permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| 256 | permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| 257 | |
| 258 | auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); |
| 259 | auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx }; |
| 260 | GpuWorkloadSketch sketch{ &gpu_ctx }; |
| 261 | |
| 262 | // Create sketch tensors |
| 263 | auto input_info = sketch.create_tensor_info(TensorInfo(input_shape, 1, data_type, data_layout)); |
| 264 | auto weight_info = sketch.create_tensor_info(TensorInfo(weights_shape, 1, data_type, data_layout)); |
| 265 | auto bias_info = sketch.create_tensor_info(TensorInfo(bias_shape, 1, bias_data_type, data_layout)); |
| 266 | auto dst_info = sketch.create_tensor_info(); |
| 267 | |
| 268 | ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr); |
| 269 | GpuOutput::create_op(sketch, ans_info, &dst_info); |
| 270 | |
| 271 | // Configure runtime |
| 272 | ClWorkloadRuntime runtime; |
| 273 | runtime.configure(sketch); |
| 274 | |
| 275 | for(auto &data : runtime.get_auxiliary_tensors()) |
| 276 | { |
Ramy Elgammal | 002e653 | 2023-01-11 18:48:04 +0000 | [diff] [blame] | 277 | CLTensor *tensor = std::get<0>(data); |
| 278 | TensorInfo info = std::get<1>(data); |
| 279 | AuxMemoryInfo aux_mem_req = std::get<2>(data); |
| 280 | tensor->allocator()->init(info, aux_mem_req.alignment); |
| 281 | tensor->allocator()->allocate(); // Use ACL allocated memory |
SiCong Li | 5a63d1e | 2023-01-06 16:28:57 +0000 | [diff] [blame] | 282 | } |
| 283 | // Construct user tensors |
| 284 | TensorType t_input{}; |
| 285 | TensorType t_weight{}; |
| 286 | TensorType t_bias{}; |
| 287 | TensorType t_dst{}; |
| 288 | |
| 289 | // Initialize user tensors |
| 290 | t_input.allocator()->init(input_info); |
| 291 | t_weight.allocator()->init(weight_info); |
| 292 | t_bias.allocator()->init(bias_info); |
| 293 | t_dst.allocator()->init(dst_info); |
| 294 | |
| 295 | ARM_COMPUTE_ASSERT(t_input.info()->is_resizable()); |
| 296 | ARM_COMPUTE_ASSERT(t_weight.info()->is_resizable()); |
| 297 | ARM_COMPUTE_ASSERT(t_bias.info()->is_resizable()); |
| 298 | ARM_COMPUTE_ASSERT(t_dst.info()->is_resizable()); |
| 299 | |
| 300 | // Allocate and fill user tensors |
| 301 | t_input.allocator()->allocate(); |
| 302 | t_weight.allocator()->allocate(); |
| 303 | t_bias.allocator()->allocate(); |
| 304 | t_dst.allocator()->allocate(); |
| 305 | |
| 306 | ARM_COMPUTE_ASSERT(!t_input.info()->is_resizable()); |
| 307 | ARM_COMPUTE_ASSERT(!t_weight.info()->is_resizable()); |
| 308 | ARM_COMPUTE_ASSERT(!t_bias.info()->is_resizable()); |
| 309 | ARM_COMPUTE_ASSERT(!t_dst.info()->is_resizable()); |
| 310 | |
| 311 | fill(AccessorType(t_input), 0); |
| 312 | fill(AccessorType(t_weight), 1); |
| 313 | fill(AccessorType(t_bias), 2); |
| 314 | |
| 315 | // Run runtime |
| 316 | runtime.run({ &t_input, &t_weight, &t_bias, &t_dst }); |
| 317 | return t_dst; |
| 318 | } |
| 319 | |
| 320 | SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, |
| 321 | DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info) |
| 322 | { |
| 323 | // Create reference |
| 324 | SimpleTensor<T> src{ input_shape, data_type, 1, quantization_info }; |
| 325 | SimpleTensor<T> weights{ weights_shape, data_type, 1, quantization_info }; |
| 326 | SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, quantization_info }; |
| 327 | |
| 328 | // Fill reference |
| 329 | fill(src, 0); |
| 330 | fill(weights, 1); |
| 331 | fill(bias, 2); |
| 332 | |
| 333 | SimpleTensor<T> dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info); |
| 334 | return dst; |
| 335 | } |
| 336 | TensorType _target{}; |
| 337 | SimpleTensor<T> _reference{}; |
| 338 | }; |
| 339 | |
| 340 | template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| 341 | class DynamicFusionDirectConv2dValidationFixture : public DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| 342 | { |
| 343 | public: |
| 344 | template <typename...> |
| 345 | void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, |
| 346 | DataLayout data_layout) |
| 347 | { |
| 348 | DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, |
| 349 | QuantizationInfo(), |
| 350 | data_layout); |
| 351 | } |
| 352 | }; |
| 353 | |
Ramy Elgammal | 73f19af | 2022-10-23 11:44:49 +0100 | [diff] [blame] | 354 | } // namespace validation |
| 355 | } // namespace test |
| 356 | } // namespace arm_compute |
Ramy Elgammal | 404462a | 2022-11-08 02:14:46 +0000 | [diff] [blame] | 357 | #endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE */ |