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Diffstat (limited to 'tests/validation/CL/IndirectConvolutionLayer.cpp')
| -rw-r--r-- | tests/validation/CL/IndirectConvolutionLayer.cpp | 268 |
1 files changed, 268 insertions, 0 deletions
diff --git a/tests/validation/CL/IndirectConvolutionLayer.cpp b/tests/validation/CL/IndirectConvolutionLayer.cpp new file mode 100644 index 0000000000..aedf070e6b --- /dev/null +++ b/tests/validation/CL/IndirectConvolutionLayer.cpp @@ -0,0 +1,268 @@ +/* + * 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 |