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731 lines
47 KiB
731 lines
47 KiB
/*
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* Copyright (c) 2017-2023 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/CL/CLTensor.h"
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#include "arm_compute/runtime/CL/CLTensorAllocator.h"
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#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/PaddingCalculator.h"
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#include "tests/datasets/DirectConvolutionLayerDataset.h"
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#include "tests/datasets/ShapeDatasets.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/fixtures/DirectConvolutionLayerFixture.h"
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/** Synced with tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp
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* Please check there for any differences in the coverage
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*/
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace
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{
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RelativeTolerance<half> tolerance_fp16(half(0.2)); /**< Tolerance for floating point tests */
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RelativeTolerance<float> tolerance_fp32(0.05f); /**< Tolerance for floating point tests */
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constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/
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constexpr float tolerance_num = 0.07f; /**< Tolerance number */
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constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1); /**< Tolerance for quantized tests */
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const auto data_strides = combine(framework::dataset::make("StrideX", 1, 3), framework::dataset::make("StrideY", 1, 3));
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const auto data_strides_small = combine(framework::dataset::make("StrideX", 1), framework::dataset::make("StrideY", 1));
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const auto data_ksize_one = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 1)));
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const auto data_ksize_one_small = combine(framework::dataset::make("PadX", 0), combine(framework::dataset::make("PadY", 0), framework::dataset::make("KernelSize", 1)));
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const auto data_ksize_three = combine(framework::dataset::make("PadX", 0, 2), combine(framework::dataset::make("PadY", 0, 2), framework::dataset::make("KernelSize", 3)));
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const auto data_ksize_five = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 5)));
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const auto data_ksize_nine = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 9)));
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const auto data_ksize_nine_small = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 9)));
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const auto data_all_kernels = concat(concat(data_ksize_one, data_ksize_three), data_ksize_five);
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const auto data = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_all_kernels));
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const auto data9x9 = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_ksize_nine));
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const auto data_small = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_one_small));
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const auto data_small9x9 = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_nine_small));
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/** Direct convolution nightly data set. */
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const auto data_nightly = combine(data, framework::dataset::make("NumKernels", { 1, 4 }));
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const auto data_nightly_9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 1, 4 }));
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const auto data_nightly_usecase = combine(framework::dataset::make("InputShape", { TensorShape{ 3U, 800U, 800U } }),
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combine(framework::dataset::make("StrideX", { 1 }),
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combine(framework::dataset::make("StrideY", { 1 }),
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combine(framework::dataset::make("PadX", { 4 }),
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combine(framework::dataset::make("PadY", { 4 }),
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combine(framework::dataset::make("KernelSize", 9),
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framework::dataset::make("NumKernels", { 16 })))))));
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/** Direct convolution precommit data set. */
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const auto data_precommit = combine(data_small, framework::dataset::make("NumKernels", { 1 }));
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const auto data_precommit_9x9 = combine(data_small9x9, framework::dataset::make("NumKernels", { 1 }));
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/** Activation function Dataset*/
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const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
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{ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) });
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} // namespace
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TEST_SUITE(CL)
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TEST_SUITE(DirectConvolutionLayer)
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/** Check whether the configuration of a Direct Convolution layer with no
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* bias leads to a successful execution.
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*/
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TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT)
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{
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const auto src_shape = TensorShape(27U, 13U, 2U);
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const auto weights_shape = TensorShape(3U, 3U, 2U, 4U);
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const auto bias_shape = TensorShape(4U);
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const auto dst_shape = TensorShape(25U, 11U, 4U);
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constexpr auto dt = DataType::F32;
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auto src = create_tensor<CLTensor>(src_shape, dt);
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auto weights = create_tensor<CLTensor>(weights_shape, dt);
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auto dst = create_tensor<CLTensor>(dst_shape, dt);
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const auto conv_info = PadStrideInfo(1, 1, 0, 0);
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// Create Direct Convolution function
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CLDirectConvolutionLayer conv{};
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conv.configure(&src, &weights, nullptr, &dst, conv_info);
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src.allocator()->allocate();
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weights.allocator()->allocate();
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dst.allocator()->allocate();
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library->fill_tensor_value(CLAccessor(src), 1.f);
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library->fill_tensor_value(CLAccessor(weights), 1.f);
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conv.run();
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// Compute reference to compare
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SimpleTensor<float> ref_src{ src_shape, dt };
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SimpleTensor<float> ref_weights{ weights_shape, dt };
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SimpleTensor<float> ref_bias{ bias_shape, dt };
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library->fill_tensor_value(ref_src, 1.f);
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library->fill_tensor_value(ref_weights, 1.f);
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// No bias
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library->fill_tensor_value(ref_bias, 0.f);
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auto ref_dst = reference::convolution_layer<float>(ref_src, ref_weights, ref_bias, dst_shape, conv_info);
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validate(CLAccessor(dst), ref_dst);
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}
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/** Check whether the case of rectangle kernels i.e. when width and height of the weight_shape are not equal
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* would lead to successful run
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*/
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TEST_CASE(NonSquareKernel, framework::DatasetMode::PRECOMMIT)
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{
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auto src_shape = TensorShape(33U, 27U, 3U);
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auto weights_shape = TensorShape(5U, 7U, 3U, 4U); // non-square kernel
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const auto bias_shape = TensorShape(4U);
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auto dst_shape = TensorShape(11U, 12U, 4U);
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constexpr auto dt = DataType::F32;
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TensorShape src_shape_nhwc(src_shape);
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TensorShape weights_shape_nhwc(weights_shape);
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TensorShape dst_shape_nhwc(dst_shape);
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// Non-square shapes are only allowed for NHWC
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permute(src_shape_nhwc, PermutationVector(2U, 0U, 1U));
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permute(weights_shape_nhwc, PermutationVector(2U, 0U, 1U));
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permute(dst_shape_nhwc, PermutationVector(2U, 0U, 1U));
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auto src = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC);
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auto weights = create_tensor<CLTensor>(weights_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC);
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auto dst = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC);
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const auto conv_info = PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR);
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// Create direct convolution function
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CLDirectConvolutionLayer conv{};
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conv.configure(&src, &weights, nullptr, &dst, conv_info);
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src.allocator()->allocate();
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weights.allocator()->allocate();
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dst.allocator()->allocate();
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library->fill_tensor_value(CLAccessor(src), 1.f);
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library->fill_tensor_value(CLAccessor(weights), 1.f);
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conv.run();
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// Compute reference to compare
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SimpleTensor<float> ref_src{ src_shape, dt };
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SimpleTensor<float> ref_weights{ weights_shape, dt };
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SimpleTensor<float> ref_bias{ bias_shape, dt };
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library->fill_tensor_value(ref_src, 1.f);
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library->fill_tensor_value(ref_weights, 1.f);
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// No bias
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library->fill_tensor_value(ref_bias, 0.f);
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auto ref_dst = reference::convolution_layer<float>(ref_src, ref_weights, ref_bias, dst_shape, conv_info);
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validate(CLAccessor(dst), ref_dst);
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}
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid: Mismatching data type input/weights
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid: Mismatching input feature maps
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported biases size
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported biases dimensions
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
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TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
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}),
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framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
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TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
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})),
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framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U), 1, DataType::F32),
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TensorInfo(TensorShape(4U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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})),
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framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
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})),
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framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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})),
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framework::dataset::make("ActivationInfo",
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{
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)
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})),
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framework::dataset::make("Expected", { false, false, false, false, false, false, true })),
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input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
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{
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bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
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ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
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}
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// clang-format on
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// *INDENT-ON*
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template <typename T>
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using CLDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
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template <typename T>
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using CLDirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T, true>;
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template <typename T>
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using CLDirectConvolutionValidationWithTensorShapesFixture = DirectConvolutionValidationWithTensorShapesFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
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template <typename T>
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using CLDirectConvolutionLayerQuantizedFixture = DirectConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
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template <typename T>
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using CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture = DirectConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T, true>;
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template <typename T>
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using CLDirectConvolutionValidationWithTensorShapesQuantizedFixture = DirectConvolutionValidationWithTensorShapesQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
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TEST_SUITE(NHWC)
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputInfo", {
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TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Arbitrary weight sizes for NHWC are supported
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TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Non-rectangular weights dimensions for NHWC are supported
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TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Strides > 2 for any kernel sizes for NHWC are supported
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}),
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framework::dataset::make("WeightsInfo",{
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TensorInfo(TensorShape(2U, 13U, 13U, 4U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(2U, 5U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
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})),
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framework::dataset::make("BiasesInfo",{
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TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
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})),
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framework::dataset::make("OutputInfo",{
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TensorInfo(TensorShape(4U, 15U, 1U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(4U, 23U, 11U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(4U, 9U, 4U), 1, DataType::F32, DataLayout::NHWC),
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})),
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framework::dataset::make("ConvInfo", {
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(3, 3, 0, 0),
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})),
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framework::dataset::make("ActivationInfo",
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{
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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})),
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framework::dataset::make("Expected", { true, true, true })),
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input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
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{
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bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
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ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
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}
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TEST_SUITE(FP16)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
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TensorShape(19U, 5U, 16U, 4U),
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TensorShape(13U, 5U, 17U, 2U),
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TensorShape(32U, 37U, 13U) } ),
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framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
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framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
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framework::dataset::make("PadX", { 1, 3, 0, 4 })),
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framework::dataset::make("PadY", { 1, 3, 0, 4 })),
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framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
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framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),
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framework::dataset::make("DataType", DataType::F16)),
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framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
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framework::dataset::make("DataLayout", DataLayout::NHWC)))
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{
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validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
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framework::dataset::make("StrideX", { 1 })),
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framework::dataset::make("StrideY", { 1 })),
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framework::dataset::make("PadX", { 1 })),
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framework::dataset::make("PadY", { 1 })),
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framework::dataset::make("KernelSize", { 9 })),
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framework::dataset::make("NumKernels", { 3 })),
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framework::dataset::make("DataType", DataType::F16)),
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framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )),
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framework::dataset::make("DataLayout", DataLayout::NHWC)))
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{
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validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
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}
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TEST_SUITE_END() // FP16
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TEST_SUITE(FP32)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
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TensorShape(19U, 5U, 16U, 4U),
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TensorShape(13U, 5U, 17U, 2U),
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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, CLDirectConvolutionLayerMixedDataLayoutFixture<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, CLDirectConvolutionLayerFixture<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(Quantized)
|
|
TEST_SUITE(QASYMM8)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
|
|
combine(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", { 7, 3, 1, 3 })),
|
|
framework::dataset::make("DataType", DataType::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", QuantizationInfo(1.1f / 255, 10))),
|
|
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
|
|
framework::dataset::make("DataLayout", DataLayout::NHWC)))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
|
|
combine(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", { 1 })),
|
|
framework::dataset::make("KernelSize", { 3 })),
|
|
framework::dataset::make("NumKernels", { 3 })),
|
|
framework::dataset::make("DataType", DataType::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", QuantizationInfo(1.1f / 255, 10))),
|
|
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
|
|
framework::dataset::make("DataLayout", DataLayout::NHWC)))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
|
|
combine(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::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))),
|
|
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
|
|
framework::dataset::make("DataLayout", DataLayout::NHWC)))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
|
|
TEST_SUITE_END() // QASYMM8
|
|
TEST_SUITE(QASYMM8_SIGNED)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT,
|
|
combine(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", { 7, 3, 1, 3 })),
|
|
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
|
|
framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))),
|
|
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
|
|
framework::dataset::make("DataLayout", DataLayout::NHWC)))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT,
|
|
combine(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", { 1 })),
|
|
framework::dataset::make("PadX", { 1 })),
|
|
framework::dataset::make("PadY", { 1 })),
|
|
framework::dataset::make("KernelSize", { 3 })),
|
|
framework::dataset::make("NumKernels", { 3 })),
|
|
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
|
|
framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))),
|
|
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
|
|
framework::dataset::make("DataLayout", DataLayout::NHWC)))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY,
|
|
combine(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::QASYMM8_SIGNED)),
|
|
framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))),
|
|
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
|
|
framework::dataset::make("DataLayout", DataLayout::NHWC)))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
TEST_SUITE_END() // QASYMM8_SIGNED
|
|
TEST_SUITE_END() // Quantized
|
|
TEST_SUITE_END() // NHWC
|
|
|
|
TEST_SUITE(NCHW)
|
|
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
|
|
framework::dataset::make("InputInfo", {
|
|
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Unsupported kernel width
|
|
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Non-rectangular weights dimensions are unsupported
|
|
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW) // Unsupported stride
|
|
}),
|
|
framework::dataset::make("WeightsInfo",{
|
|
TensorInfo(TensorShape(11U, 11U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW),
|
|
TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW),
|
|
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW)
|
|
})),
|
|
framework::dataset::make("BiasesInfo",{
|
|
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW),
|
|
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW),
|
|
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW)
|
|
})),
|
|
framework::dataset::make("OutputInfo",{
|
|
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW),
|
|
TensorInfo(TensorShape(23U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW),
|
|
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW)
|
|
})),
|
|
framework::dataset::make("ConvInfo", {
|
|
PadStrideInfo(1, 1, 0, 0),
|
|
PadStrideInfo(1, 1, 0, 0),
|
|
PadStrideInfo(3, 3, 0, 0)
|
|
})),
|
|
framework::dataset::make("ActivationInfo",
|
|
{
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)
|
|
})),
|
|
framework::dataset::make("Expected", { false, false, false})),
|
|
input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
|
|
{
|
|
bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
|
|
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
|
|
}
|
|
// clang-format on
|
|
// *INDENT-ON*
|
|
|
|
TEST_SUITE(Float)
|
|
TEST_SUITE(FP16)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType", DataType::F16)),
|
|
ActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", DataLayout::NCHW)))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType", DataType::F16)),
|
|
ActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", DataLayout::NCHW)))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
|
|
}
|
|
TEST_SUITE_END() // FP16
|
|
|
|
TEST_SUITE(FP32)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
|
|
DataType::F32)),
|
|
ActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit,
|
|
framework::dataset::make("DataType",
|
|
DataType::F32)),
|
|
ActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType", DataType::F32)),
|
|
ActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
|
|
}
|
|
TEST_SUITE_END() // FP32
|
|
|
|
TEST_SUITE(FP32_CustomDataset)
|
|
FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::DirectConvolutionLayerDataset(),
|
|
framework::dataset::make("DataType", DataType::F32)),
|
|
ActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
|
|
}
|
|
TEST_SUITE_END() // FP32_CustomDataset
|
|
TEST_SUITE_END() // Float
|
|
|
|
const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
|
|
{
|
|
ActivationLayerInfo(),
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
|
|
});
|
|
TEST_SUITE(Quantized)
|
|
TEST_SUITE(QASYMM8)
|
|
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(data_precommit,
|
|
framework::dataset::make("DataType", DataType::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10) })),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(data_precommit,
|
|
framework::dataset::make("DataType", DataType::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(data_precommit_9x9,
|
|
framework::dataset::make("DataType",
|
|
DataType::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(3.f / 255, 10), QuantizationInfo(1.1f, 10) })),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(data_nightly, framework::dataset::make("DataType",
|
|
DataType::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunLarge9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(data_nightly_9x9,
|
|
framework::dataset::make("DataType",
|
|
DataType::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(3.f / 255, 10), QuantizationInfo(1.1f, 10) })),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
|
|
TEST_SUITE_END() // QASYMM8
|
|
|
|
TEST_SUITE(QASYMM8_CustomDataset)
|
|
FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
|
|
combine(combine(combine(combine(datasets::DirectConvolutionLayerDataset(),
|
|
framework::dataset::make("DataType", DataType::QASYMM8)),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) })),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
TEST_SUITE_END() // QASYMM8_CustomDataset
|
|
|
|
TEST_SUITE(QASYMM8_SIGNED)
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(data_precommit, framework::dataset::make("DataType",
|
|
DataType::QASYMM8_SIGNED)),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, -10) })),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
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}
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FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(data_precommit,
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|
framework::dataset::make("DataType",
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|
DataType::QASYMM8_SIGNED)),
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|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.1f, -10) })),
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|
QuantizedActivationFunctionsDataset),
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|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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|
{
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|
// Validate output
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|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
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|
}
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|
FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(data_precommit_9x9,
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|
framework::dataset::make("DataType",
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|
DataType::QASYMM8_SIGNED)),
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|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })),
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|
QuantizedActivationFunctionsDataset),
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|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
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|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunCustomDataset, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY,
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|
combine(combine(combine(combine(datasets::DirectConvolutionLayerDataset(),
|
|
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) })),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
|
|
}
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|
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|
TEST_SUITE_END() // QASYMM8_SIGNED
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|
TEST_SUITE_END() // Quantized
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|
TEST_SUITE_END() // NCHW
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|
TEST_SUITE_END() // DirectConvolutionLayer
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|
TEST_SUITE_END() // CL
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|
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|
} // namespace validation
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|
} // namespace test
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|
} // namespace arm_compute
|