Gian Marco Iodice | 76335eb | 2022-11-17 11:03:39 +0000 | [diff] [blame] | 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 | #include "arm_compute/core/Types.h" |
| 25 | #include "arm_compute/runtime/CL/CLTensor.h" |
| 26 | #include "arm_compute/runtime/CL/CLTensorAllocator.h" |
| 27 | #include "arm_compute/runtime/CL/functions/CLIndirectConvolutionLayer.h" |
| 28 | #include "tests/CL/CLAccessor.h" |
| 29 | #include "tests/datasets/ShapeDatasets.h" |
| 30 | #include "tests/framework/Macros.h" |
| 31 | #include "tests/validation/Validation.h" |
| 32 | #include "tests/validation/fixtures/DirectConvolutionLayerFixture.h" |
| 33 | |
| 34 | // Note: Since the interface of indirect convolution is the same of direct convolution, we can reuse |
| 35 | // the direct convolution fixture |
| 36 | |
| 37 | namespace arm_compute |
| 38 | { |
| 39 | namespace test |
| 40 | { |
| 41 | namespace validation |
| 42 | { |
| 43 | namespace |
| 44 | { |
| 45 | RelativeTolerance<half> tolerance_fp16(half(0.2)); /**< Tolerance for floating point tests */ |
| 46 | RelativeTolerance<float> tolerance_fp32(0.05f); /**< Tolerance for floating point tests */ |
| 47 | constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/ |
| 48 | constexpr float tolerance_num = 0.07f; /**< Tolerance number */ |
| 49 | |
| 50 | /** Activation function Dataset*/ |
| 51 | const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| 52 | { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) }); |
| 53 | } // namespace |
| 54 | |
| 55 | TEST_SUITE(CL) |
| 56 | TEST_SUITE(IndirectConvolutionLayer) |
| 57 | |
| 58 | /** Check whether the configuration of a indirect convolution layer with no |
| 59 | * bias leads to a successful run. |
| 60 | */ |
| 61 | TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT) |
| 62 | { |
| 63 | const TensorShape src_shape_nhwc = TensorShape(8U, 27U, 13U); |
| 64 | const TensorShape wei_shape_nhwc = TensorShape(8U, 3U, 3U, 4U); |
| 65 | const TensorShape bia_shape = TensorShape(4U); |
| 66 | const TensorShape dst_shape_nhwc = TensorShape(4U, 25U, 11U); |
| 67 | constexpr DataType dt = DataType::F32; |
| 68 | constexpr DataLayout data_layout = DataLayout::NHWC; |
| 69 | |
| 70 | auto src_nhwc = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| 71 | auto wei_nhwc = create_tensor<CLTensor>(wei_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| 72 | auto dst_nhwc = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| 73 | |
| 74 | TensorShape src_shape_nchw = src_shape_nhwc; |
| 75 | TensorShape wei_shape_nchw = wei_shape_nhwc; |
| 76 | TensorShape dst_shape_nchw = dst_shape_nhwc; |
| 77 | |
| 78 | permute(src_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| 79 | permute(wei_shape_nchw, PermutationVector(1U, 2U, 0U, 3U)); |
| 80 | permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| 81 | |
| 82 | const PadStrideInfo conv_info = PadStrideInfo(1, 1, 0, 0); |
| 83 | |
| 84 | // Create indirect Convolution function |
| 85 | CLIndirectConvolutionLayer conv{}; |
| 86 | conv.configure(&src_nhwc, &wei_nhwc, nullptr, &dst_nhwc, conv_info); |
| 87 | |
| 88 | src_nhwc.allocator()->allocate(); |
| 89 | wei_nhwc.allocator()->allocate(); |
| 90 | dst_nhwc.allocator()->allocate(); |
| 91 | |
| 92 | library->fill_tensor_value(CLAccessor(src_nhwc), 1.f); |
| 93 | library->fill_tensor_value(CLAccessor(wei_nhwc), 1.f); |
| 94 | |
| 95 | conv.run(); |
| 96 | |
| 97 | // Compute reference to compare |
| 98 | SimpleTensor<float> ref_src{ src_shape_nchw, dt }; |
| 99 | SimpleTensor<float> ref_wei{ wei_shape_nchw, dt }; |
| 100 | SimpleTensor<float> ref_bia{ bia_shape, dt }; |
| 101 | library->fill_tensor_value(ref_src, 1.f); |
| 102 | library->fill_tensor_value(ref_wei, 1.f); |
| 103 | // No bias |
| 104 | library->fill_tensor_value(ref_bia, 0.f); |
| 105 | auto ref_dst = reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info); |
| 106 | |
| 107 | validate(CLAccessor(dst_nhwc), ref_dst); |
| 108 | } |
| 109 | |
| 110 | /** Check whether the case of rectangle kernels i.e. when width and height of the weight_shape are not equal |
| 111 | * would lead to successful run |
| 112 | */ |
| 113 | TEST_CASE(NonSquareKernel, framework::DatasetMode::PRECOMMIT) |
| 114 | { |
| 115 | const TensorShape src_shape_nhwc = TensorShape(3U, 33U, 27U); |
| 116 | const TensorShape wei_shape_nhwc = TensorShape(3U, 5U, 7U, 4U); // non-square kernel |
| 117 | const TensorShape bia_shape = TensorShape(4U); |
| 118 | const TensorShape dst_shape_nhwc = TensorShape(4U, 11U, 12U); |
| 119 | constexpr DataType dt = DataType::F32; |
| 120 | constexpr DataLayout data_layout = DataLayout::NHWC; |
| 121 | |
| 122 | auto src_nhwc = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| 123 | auto wei_nhwc = create_tensor<CLTensor>(wei_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| 124 | auto dst_nhwc = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| 125 | |
| 126 | TensorShape src_shape_nchw = src_shape_nhwc; |
| 127 | TensorShape wei_shape_nchw = wei_shape_nhwc; |
| 128 | TensorShape dst_shape_nchw = dst_shape_nhwc; |
| 129 | |
| 130 | permute(src_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| 131 | permute(wei_shape_nchw, PermutationVector(1U, 2U, 0U, 3U)); |
| 132 | permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| 133 | |
| 134 | const PadStrideInfo conv_info = PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR); |
| 135 | |
| 136 | // Create indirect convolution function |
| 137 | CLIndirectConvolutionLayer conv{}; |
| 138 | conv.configure(&src_nhwc, &wei_nhwc, nullptr, &dst_nhwc, conv_info); |
| 139 | |
| 140 | src_nhwc.allocator()->allocate(); |
| 141 | wei_nhwc.allocator()->allocate(); |
| 142 | dst_nhwc.allocator()->allocate(); |
| 143 | |
| 144 | library->fill_tensor_value(CLAccessor(src_nhwc), 1.f); |
| 145 | library->fill_tensor_value(CLAccessor(wei_nhwc), 1.f); |
| 146 | |
| 147 | conv.run(); |
| 148 | |
| 149 | // Compute reference to compare |
| 150 | SimpleTensor<float> ref_src{ src_shape_nchw, dt }; |
| 151 | SimpleTensor<float> ref_wei{ wei_shape_nchw, dt }; |
| 152 | SimpleTensor<float> ref_bia{ bia_shape, dt }; |
| 153 | library->fill_tensor_value(ref_src, 1.f); |
| 154 | library->fill_tensor_value(ref_wei, 1.f); |
| 155 | // No bias |
| 156 | library->fill_tensor_value(ref_bia, 0.f); |
| 157 | auto ref_dst = reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info); |
| 158 | |
| 159 | validate(CLAccessor(dst_nhwc), ref_dst); |
| 160 | } |
| 161 | // *INDENT-OFF* |
| 162 | // clang-format off |
| 163 | // Note: Since the interface of indirect convolution is the same of direct convolution, we can reuse |
| 164 | // the direct convolution fixture |
| 165 | template <typename T> |
| 166 | using CLIndirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLIndirectConvolutionLayer, T>; |
| 167 | template <typename T> |
| 168 | using CLIndirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLIndirectConvolutionLayer, T, true>; |
| 169 | |
| 170 | TEST_SUITE(NHWC) |
| 171 | TEST_SUITE(FP16) |
| 172 | FIXTURE_DATA_TEST_CASE(RunSmall, CLIndirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, |
| 173 | combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| 174 | framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), |
| 175 | TensorShape(19U, 5U, 16U, 4U), |
| 176 | TensorShape(13U, 5U, 17U, 2U), |
| 177 | TensorShape(32U, 37U, 13U) } ), |
| 178 | framework::dataset::make("StrideX", { 1, 3, 1, 1 })), |
| 179 | framework::dataset::make("StrideY", { 1, 3, 2, 1 })), |
| 180 | framework::dataset::make("PadX", { 1, 3, 0, 4 })), |
| 181 | framework::dataset::make("PadY", { 1, 3, 0, 4 })), |
| 182 | framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), |
| 183 | framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), |
| 184 | framework::dataset::make("DataType", DataType::F16)), |
| 185 | framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), |
| 186 | framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| 187 | { |
| 188 | validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num); |
| 189 | } |
| 190 | |
| 191 | FIXTURE_DATA_TEST_CASE(RunLarge, CLIndirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, |
| 192 | combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| 193 | framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), |
| 194 | framework::dataset::make("StrideX", { 1 })), |
| 195 | framework::dataset::make("StrideY", { 1 })), |
| 196 | framework::dataset::make("PadX", { 1 })), |
| 197 | framework::dataset::make("PadY", { 1 })), |
| 198 | framework::dataset::make("KernelSize", { 9 })), |
| 199 | framework::dataset::make("NumKernels", { 3 })), |
| 200 | framework::dataset::make("DataType", DataType::F16)), |
| 201 | framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )), |
| 202 | framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| 203 | { |
| 204 | validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num); |
| 205 | } |
| 206 | |
| 207 | TEST_SUITE_END() // FP16 |
| 208 | |
| 209 | TEST_SUITE(FP32) |
| 210 | FIXTURE_DATA_TEST_CASE(RunSmall, CLIndirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| 211 | combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| 212 | framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), |
| 213 | TensorShape(19U, 5U, 16U, 4U), |
| 214 | TensorShape(13U, 5U, 17U, 2U), |
| 215 | TensorShape(32U, 37U, 13U) } ), |
| 216 | framework::dataset::make("StrideX", { 1, 3, 1, 1 })), |
| 217 | framework::dataset::make("StrideY", { 1, 3, 2, 1 })), |
| 218 | framework::dataset::make("PadX", { 1, 3, 0, 4 })), |
| 219 | framework::dataset::make("PadY", { 1, 3, 0, 4 })), |
| 220 | framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), |
| 221 | framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), |
| 222 | framework::dataset::make("DataType", DataType::F32)), |
| 223 | framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), |
| 224 | framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| 225 | { |
| 226 | validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); |
| 227 | } |
| 228 | FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLIndirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, |
| 229 | combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| 230 | framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), |
| 231 | TensorShape(19U, 5U, 16U, 4U), |
| 232 | TensorShape(13U, 5U, 17U, 2U), |
| 233 | TensorShape(32U, 37U, 13U) } ), |
| 234 | framework::dataset::make("StrideX", { 1 })), |
| 235 | framework::dataset::make("StrideY", { 2 })), |
| 236 | framework::dataset::make("PadX", { 1 })), |
| 237 | framework::dataset::make("PadY", { 3 })), |
| 238 | framework::dataset::make("KernelSize", { 3 })), |
| 239 | framework::dataset::make("NumKernels", { 3 })), |
| 240 | framework::dataset::make("DataType", DataType::F32)), |
| 241 | framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), |
| 242 | framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| 243 | { |
| 244 | validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); |
| 245 | } |
| 246 | FIXTURE_DATA_TEST_CASE(RunLarge, CLIndirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| 247 | combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| 248 | framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), |
| 249 | framework::dataset::make("StrideX", { 1 })), |
| 250 | framework::dataset::make("StrideY", { 1 })), |
| 251 | framework::dataset::make("PadX", { 1 })), |
| 252 | framework::dataset::make("PadY", { 1 })), |
| 253 | framework::dataset::make("KernelSize", { 9 })), |
| 254 | framework::dataset::make("NumKernels", { 3 })), |
| 255 | framework::dataset::make("DataType", DataType::F32)), |
| 256 | framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )), |
| 257 | framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| 258 | { |
| 259 | validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); |
| 260 | } |
| 261 | TEST_SUITE_END() // FP32 |
| 262 | TEST_SUITE_END() // NHWC |
| 263 | TEST_SUITE_END() // IndirectConvolutionLayer |
| 264 | TEST_SUITE_END() // CL |
| 265 | |
| 266 | } // namespace validation |
| 267 | } // namespace test |
| 268 | } // namespace arm_compute |