| /* |
| * Copyright (c) 2022 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. |
| */ |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/CL/CLTensor.h" |
| #include "arm_compute/runtime/CL/CLTensorAllocator.h" |
| #include "arm_compute/runtime/CL/functions/CLIndirectConvolutionLayer.h" |
| #include "tests/CL/CLAccessor.h" |
| #include "tests/datasets/ShapeDatasets.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/fixtures/DirectConvolutionLayerFixture.h" |
| |
| // Note: Since the interface of indirect convolution is the same of direct convolution, we can reuse |
| // the direct convolution fixture |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| RelativeTolerance<half> tolerance_fp16(half(0.2)); /**< Tolerance for floating point tests */ |
| RelativeTolerance<float> tolerance_fp32(0.05f); /**< Tolerance for floating point tests */ |
| constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/ |
| constexpr float tolerance_num = 0.07f; /**< Tolerance number */ |
| |
| /** Activation function Dataset*/ |
| const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) }); |
| } // namespace |
| |
| TEST_SUITE(CL) |
| TEST_SUITE(IndirectConvolutionLayer) |
| |
| /** Check whether the configuration of a indirect convolution layer with no |
| * bias leads to a successful run. |
| */ |
| TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT) |
| { |
| const TensorShape src_shape_nhwc = TensorShape(8U, 27U, 13U); |
| const TensorShape wei_shape_nhwc = TensorShape(8U, 3U, 3U, 4U); |
| const TensorShape bia_shape = TensorShape(4U); |
| const TensorShape dst_shape_nhwc = TensorShape(4U, 25U, 11U); |
| constexpr DataType dt = DataType::F32; |
| constexpr DataLayout data_layout = DataLayout::NHWC; |
| |
| auto src_nhwc = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| auto wei_nhwc = create_tensor<CLTensor>(wei_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| auto dst_nhwc = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| |
| TensorShape src_shape_nchw = src_shape_nhwc; |
| TensorShape wei_shape_nchw = wei_shape_nhwc; |
| TensorShape dst_shape_nchw = dst_shape_nhwc; |
| |
| permute(src_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| permute(wei_shape_nchw, PermutationVector(1U, 2U, 0U, 3U)); |
| permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| |
| const PadStrideInfo conv_info = PadStrideInfo(1, 1, 0, 0); |
| |
| // Create indirect Convolution function |
| CLIndirectConvolutionLayer conv{}; |
| conv.configure(&src_nhwc, &wei_nhwc, nullptr, &dst_nhwc, conv_info); |
| |
| src_nhwc.allocator()->allocate(); |
| wei_nhwc.allocator()->allocate(); |
| dst_nhwc.allocator()->allocate(); |
| |
| library->fill_tensor_value(CLAccessor(src_nhwc), 1.f); |
| library->fill_tensor_value(CLAccessor(wei_nhwc), 1.f); |
| |
| conv.run(); |
| |
| // Compute reference to compare |
| SimpleTensor<float> ref_src{ src_shape_nchw, dt }; |
| SimpleTensor<float> ref_wei{ wei_shape_nchw, dt }; |
| SimpleTensor<float> ref_bia{ bia_shape, dt }; |
| library->fill_tensor_value(ref_src, 1.f); |
| library->fill_tensor_value(ref_wei, 1.f); |
| // No bias |
| library->fill_tensor_value(ref_bia, 0.f); |
| auto ref_dst = reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info); |
| |
| validate(CLAccessor(dst_nhwc), ref_dst); |
| } |
| |
| /** Check whether the case of rectangle kernels i.e. when width and height of the weight_shape are not equal |
| * would lead to successful run |
| */ |
| TEST_CASE(NonSquareKernel, framework::DatasetMode::PRECOMMIT) |
| { |
| const TensorShape src_shape_nhwc = TensorShape(3U, 33U, 27U); |
| const TensorShape wei_shape_nhwc = TensorShape(3U, 5U, 7U, 4U); // non-square kernel |
| const TensorShape bia_shape = TensorShape(4U); |
| const TensorShape dst_shape_nhwc = TensorShape(4U, 11U, 12U); |
| constexpr DataType dt = DataType::F32; |
| constexpr DataLayout data_layout = DataLayout::NHWC; |
| |
| auto src_nhwc = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| auto wei_nhwc = create_tensor<CLTensor>(wei_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| auto dst_nhwc = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), data_layout); |
| |
| TensorShape src_shape_nchw = src_shape_nhwc; |
| TensorShape wei_shape_nchw = wei_shape_nhwc; |
| TensorShape dst_shape_nchw = dst_shape_nhwc; |
| |
| permute(src_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| permute(wei_shape_nchw, PermutationVector(1U, 2U, 0U, 3U)); |
| permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| |
| const PadStrideInfo conv_info = PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR); |
| |
| // Create indirect convolution function |
| CLIndirectConvolutionLayer conv{}; |
| conv.configure(&src_nhwc, &wei_nhwc, nullptr, &dst_nhwc, conv_info); |
| |
| src_nhwc.allocator()->allocate(); |
| wei_nhwc.allocator()->allocate(); |
| dst_nhwc.allocator()->allocate(); |
| |
| library->fill_tensor_value(CLAccessor(src_nhwc), 1.f); |
| library->fill_tensor_value(CLAccessor(wei_nhwc), 1.f); |
| |
| conv.run(); |
| |
| // Compute reference to compare |
| SimpleTensor<float> ref_src{ src_shape_nchw, dt }; |
| SimpleTensor<float> ref_wei{ wei_shape_nchw, dt }; |
| SimpleTensor<float> ref_bia{ bia_shape, dt }; |
| library->fill_tensor_value(ref_src, 1.f); |
| library->fill_tensor_value(ref_wei, 1.f); |
| // No bias |
| library->fill_tensor_value(ref_bia, 0.f); |
| auto ref_dst = reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info); |
| |
| validate(CLAccessor(dst_nhwc), ref_dst); |
| } |
| // *INDENT-OFF* |
| // clang-format off |
| // Note: Since the interface of indirect convolution is the same of direct convolution, we can reuse |
| // the direct convolution fixture |
| template <typename T> |
| using CLIndirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLIndirectConvolutionLayer, T>; |
| template <typename T> |
| using CLIndirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLIndirectConvolutionLayer, T, true>; |
| |
| TEST_SUITE(NHWC) |
| TEST_SUITE(FP16) |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLIndirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), |
| TensorShape(19U, 5U, 16U, 4U), |
| TensorShape(13U, 5U, 17U, 2U), |
| TensorShape(32U, 37U, 13U) } ), |
| framework::dataset::make("StrideX", { 1, 3, 1, 1 })), |
| framework::dataset::make("StrideY", { 1, 3, 2, 1 })), |
| framework::dataset::make("PadX", { 1, 3, 0, 4 })), |
| framework::dataset::make("PadY", { 1, 3, 0, 4 })), |
| framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), |
| framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), |
| framework::dataset::make("DataType", DataType::F16)), |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLIndirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), |
| framework::dataset::make("StrideX", { 1 })), |
| framework::dataset::make("StrideY", { 1 })), |
| framework::dataset::make("PadX", { 1 })), |
| framework::dataset::make("PadY", { 1 })), |
| framework::dataset::make("KernelSize", { 9 })), |
| framework::dataset::make("NumKernels", { 3 })), |
| framework::dataset::make("DataType", DataType::F16)), |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )), |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num); |
| } |
| |
| TEST_SUITE_END() // FP16 |
| |
| TEST_SUITE(FP32) |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLIndirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), |
| TensorShape(19U, 5U, 16U, 4U), |
| TensorShape(13U, 5U, 17U, 2U), |
| TensorShape(32U, 37U, 13U) } ), |
| framework::dataset::make("StrideX", { 1, 3, 1, 1 })), |
| framework::dataset::make("StrideY", { 1, 3, 2, 1 })), |
| framework::dataset::make("PadX", { 1, 3, 0, 4 })), |
| framework::dataset::make("PadY", { 1, 3, 0, 4 })), |
| framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), |
| framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLIndirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), |
| TensorShape(19U, 5U, 16U, 4U), |
| TensorShape(13U, 5U, 17U, 2U), |
| TensorShape(32U, 37U, 13U) } ), |
| framework::dataset::make("StrideX", { 1 })), |
| framework::dataset::make("StrideY", { 2 })), |
| framework::dataset::make("PadX", { 1 })), |
| framework::dataset::make("PadY", { 3 })), |
| framework::dataset::make("KernelSize", { 3 })), |
| framework::dataset::make("NumKernels", { 3 })), |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )), |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLIndirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), |
| framework::dataset::make("StrideX", { 1 })), |
| framework::dataset::make("StrideY", { 1 })), |
| framework::dataset::make("PadX", { 1 })), |
| framework::dataset::make("PadY", { 1 })), |
| framework::dataset::make("KernelSize", { 9 })), |
| framework::dataset::make("NumKernels", { 3 })), |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )), |
| framework::dataset::make("DataLayout", DataLayout::NHWC))) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32); |
| } |
| TEST_SUITE_END() // FP32 |
| TEST_SUITE_END() // NHWC |
| TEST_SUITE_END() // IndirectConvolutionLayer |
| TEST_SUITE_END() // CL |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |