438 lines
29 KiB
C++
438 lines
29 KiB
C++
/*
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* Copyright (c) 2017-2020 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/core/utils/misc/ShapeCalculator.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/CLDeconvolutionLayer.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/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/DeconvolutionLayerFixture.h"
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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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constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f); /**< Tolerance for floating point tests */
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RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's for DataType::F16 */
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constexpr AbsoluteTolerance<float> tolerance_qasymm8(1.0); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */
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constexpr float tolerance_num = 0.07f; /**< Tolerance number */
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const auto data9x9_small_asymm = framework::dataset::make("InputShape", TensorShape{ 10U, 10U, 1U, 1U }) *framework::dataset::make("StrideX", 2) *framework::dataset::make("StrideY",
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2)
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*framework::dataset::make("PadLeft", 3)
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*framework::dataset::make("PadRight", 4) *framework::dataset::make("PadTop", 3) *framework::dataset::make("PadBottom", 4) *framework::dataset::make("NumKernels", { 1 });
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const auto data9x9_large_asymm = framework::dataset::make("InputShape", TensorShape{ 640U, 360U, 56U, 1U }) *framework::dataset::make("StrideX", 2) *framework::dataset::make("StrideY",
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2)
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*framework::dataset::make("PadLeft", 3)
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*framework::dataset::make("PadRight", 4) *framework::dataset::make("PadTop", 3) *framework::dataset::make("PadBottom", 4) *framework::dataset::make("NumKernels", { 1 });
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const auto data4x4 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 3)
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* framework::dataset::make("PadY", 0, 3) * framework::dataset::make("NumKernels", { 3 });
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const auto data3x3 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 2)
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* framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 });
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const auto data3x3_asymm = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 2) * framework::dataset::make("StrideY", 1, 2) * framework::dataset::make("PadLeft", 0, 1)
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* framework::dataset::make("PadRight", 0, 1) * framework::dataset::make("PadTop", 0, 1) * framework::dataset::make("PadBottom", 0, 1) * framework::dataset::make("NumKernels", { 3 });
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const auto data3x3_precommit = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 2) * framework::dataset::make("StrideY", 1, 2) * framework::dataset::make("PadX", 0, 2)
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* framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 });
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const auto data2x2_precommit = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 2) * framework::dataset::make("StrideY", 2) * framework::dataset::make("PadX", 1)
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* framework::dataset::make("PadY", 1) * framework::dataset::make("NumKernels", { 3 });
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const auto data1x1 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 1)
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* framework::dataset::make("PadY", 0, 1) * framework::dataset::make("NumKernels", { 3 });
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const auto data_layouts_dataset = framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC });
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const auto add_bias_dataset = framework::dataset::make("AddBias", { true, false });
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} // namespace
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TEST_SUITE(CL)
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TEST_SUITE(DeconvolutionLayer)
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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(
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framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights shape
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), // Non supported data type
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid bias shape
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TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32), // Window shrink
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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, 2U), 1, DataType::F16),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16),
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TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 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("BiasInfo", { TensorInfo(TensorShape(1U), 1, DataType::F16),
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TensorInfo(TensorShape(1U), 1, DataType::F32),
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TensorInfo(TensorShape(1U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U), 1, DataType::F32),
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TensorInfo(TensorShape(1U), 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, 2U), 1, DataType::F16),
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TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 9U, 1U, 3U), 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("PadStrideInfo", { 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, 1, 1),
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PadStrideInfo(1, 1, 0, 0),
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})),
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framework::dataset::make("Expected", { false, false, false, false, false, true })),
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input_info, weights_info, bias_info, output_info, pad_info, expected)
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{
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bool is_valid = bool(CLDeconvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pad_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 CLDeconvolutionLayerFixture4x4 = DeconvolutionValidationFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 4, 4>;
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template <typename T>
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using CLDeconvolutionLayerFixture3x3 = DeconvolutionValidationFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 3, 3>;
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template <typename T>
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using CLDeconvolutionLayerAsymmFixture3x3 = DeconvolutionValidationAsymmFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 3, 3>;
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template <typename T>
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using CLDeconvolutionLayerFixture2x2 = DeconvolutionValidationFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 2, 2>;
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template <typename T>
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using CLDeconvolutionLayerFixture1x1 = DeconvolutionValidationFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 1, 1>;
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template <typename T>
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using CLDeconvolutionLayerAsymmFixture9x9 = DeconvolutionValidationAsymmFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 9, 9>;
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TEST_SUITE(Float)
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TEST_SUITE(FP32)
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TEST_SUITE(W4x4)
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FIXTURE_DATA_TEST_CASE(Run, CLDeconvolutionLayerFixture4x4<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data4x4, framework::dataset::make("DataType", DataType::F32)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_fp32);
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}
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TEST_SUITE_END() // W4x4
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TEST_SUITE(W3x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerFixture3x3<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data3x3_precommit, framework::dataset::make("DataType",
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DataType::F32)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_fp32);
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}
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FIXTURE_DATA_TEST_CASE(RunAsymm, CLDeconvolutionLayerAsymmFixture3x3<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data3x3_asymm, framework::dataset::make("DataType",
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DataType::F32)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_fp32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLDeconvolutionLayerFixture3x3<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data3x3, framework::dataset::make("DataType", DataType::F32)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_fp32);
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}
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TEST_SUITE_END() // W3x3
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TEST_SUITE(W2x2)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerFixture2x2<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data2x2_precommit, framework::dataset::make("DataType",
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DataType::F32)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_fp32);
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}
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TEST_SUITE_END() // W2x2
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TEST_SUITE(W1x1)
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FIXTURE_DATA_TEST_CASE(Run, CLDeconvolutionLayerFixture1x1<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data1x1, framework::dataset::make("DataType", DataType::F32)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_fp32);
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}
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TEST_SUITE_END() // W1x1
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TEST_SUITE(W9x9)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerAsymmFixture9x9<float>, framework::DatasetMode::ALL, combine(combine(combine(data9x9_small_asymm, framework::dataset::make("DataType",
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DataType::F32)),
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framework::dataset::make("DataLayout", { DataLayout::NHWC })),
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framework::dataset::make("AddBias", { false })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_fp32);
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}
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TEST_SUITE_END() // W9x9
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TEST_SUITE_END() // FP32
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TEST_SUITE(FP16)
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TEST_SUITE(W4x4)
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FIXTURE_DATA_TEST_CASE(Run, CLDeconvolutionLayerFixture4x4<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data4x4, framework::dataset::make("DataType", DataType::F16)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
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}
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TEST_SUITE_END() // W4x4
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TEST_SUITE(W3x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerFixture3x3<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data3x3_precommit, framework::dataset::make("DataType",
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DataType::F16)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLDeconvolutionLayerFixture3x3<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data3x3, framework::dataset::make("DataType", DataType::F16)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
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}
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TEST_SUITE_END() // W3x3
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TEST_SUITE(W2x2)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerFixture2x2<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data2x2_precommit, framework::dataset::make("DataType",
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DataType::F16)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_f16);
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}
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TEST_SUITE_END() // W2x2
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TEST_SUITE(W1x1)
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FIXTURE_DATA_TEST_CASE(Run, CLDeconvolutionLayerFixture1x1<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data1x1, framework::dataset::make("DataType", DataType::F16)),
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data_layouts_dataset),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
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}
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TEST_SUITE_END() // W1x1
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TEST_SUITE_END() // FP16
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TEST_SUITE_END() // Float
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template <typename T>
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using CLDeconvolutionLayerQuantizedFixture4x4 = DeconvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 4, 4>;
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template <typename T>
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using CLDeconvolutionLayerQuantizedFixture3x3 = DeconvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 3, 3>;
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template <typename T>
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using CLDeconvolutionLayerQuantizedFixture2x2 = DeconvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 2, 2>;
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template <typename T>
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using CLDeconvolutionLayerQuantizedFixture1x1 = DeconvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDeconvolutionLayer, T, 1, 1>;
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TEST_SUITE(Quantized)
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TEST_SUITE(QASYMM8)
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TEST_SUITE(W4x4)
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FIXTURE_DATA_TEST_CASE(Run, CLDeconvolutionLayerQuantizedFixture4x4<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data4x4, framework::dataset::make("DataType",
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DataType::QASYMM8)),
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data_layouts_dataset),
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framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 10), QuantizationInfo(2.f / 255.f, 5) })),
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framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 5), QuantizationInfo(4.f / 255.f, 10) })),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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}
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TEST_SUITE_END() // W4x4
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TEST_SUITE(W3x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerQuantizedFixture3x3<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(data3x3_precommit,
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framework::dataset::make("DataType",
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DataType::QASYMM8)),
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data_layouts_dataset),
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framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 10), QuantizationInfo(2.f / 255.f, 4) })),
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framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 10), QuantizationInfo(4.f / 255.f, 5) })),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLDeconvolutionLayerQuantizedFixture3x3<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data3x3,
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framework::dataset::make("DataType",
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DataType::QASYMM8)),
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data_layouts_dataset),
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framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 10), QuantizationInfo(2.f / 255.f, 128) })),
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framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 128), QuantizationInfo(4.f / 255.f, 128) })),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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}
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TEST_SUITE_END() // W3x3
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TEST_SUITE(W2x2)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerQuantizedFixture2x2<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(data2x2_precommit,
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framework::dataset::make("DataType", DataType::QASYMM8)),
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data_layouts_dataset),
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framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 128), QuantizationInfo(2.f / 255.f, 128) })),
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framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 64), QuantizationInfo(4.f / 255.f, 128) })),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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}
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TEST_SUITE_END() // W2x2
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TEST_SUITE(W1x1)
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FIXTURE_DATA_TEST_CASE(Run, CLDeconvolutionLayerQuantizedFixture1x1<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data1x1, framework::dataset::make("DataType",
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DataType::QASYMM8)),
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data_layouts_dataset),
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framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 0), QuantizationInfo(2.f / 255.f, 0) })),
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framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 0), QuantizationInfo(4.f / 255.f, 0) })),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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}
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TEST_SUITE_END() // W1x1
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TEST_SUITE_END() // QASYMM8
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TEST_SUITE(QASYMM8_SIGNED)
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// QASYMM8_SIGNED: zero-point in range [-128, 127]
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// QASYMM8 : zero-point in range [0 , 255]
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TEST_SUITE(W4x4)
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FIXTURE_DATA_TEST_CASE(Run, CLDeconvolutionLayerQuantizedFixture4x4<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data4x4, framework::dataset::make("DataType",
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DataType::QASYMM8_SIGNED)),
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data_layouts_dataset),
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framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 10), QuantizationInfo(2.f / 255.f, 5) })),
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framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 5), QuantizationInfo(4.f / 255.f, 10) })),
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add_bias_dataset))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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}
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TEST_SUITE_END() // W4x4
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TEST_SUITE(W3x3)
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// DirectDeconvolution
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerQuantizedFixture3x3<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(data3x3_precommit,
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framework::dataset::make("DataType",
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DataType::QASYMM8_SIGNED)),
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|
data_layouts_dataset),
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framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 10), QuantizationInfo(2.f / 255.f, 4) })),
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framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 10), QuantizationInfo(4.f / 255.f, 5) })),
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|
add_bias_dataset))
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{
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// Validate output
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|
validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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}
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|
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FIXTURE_DATA_TEST_CASE(RunLarge, CLDeconvolutionLayerQuantizedFixture3x3<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data3x3,
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|
framework::dataset::make("DataType",
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|
DataType::QASYMM8_SIGNED)),
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|
data_layouts_dataset),
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|
framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, -10), QuantizationInfo(2.f / 255.f, 127) })),
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|
framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 64), QuantizationInfo(4.f / 255.f, -128) })),
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|
add_bias_dataset))
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|
{
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|
// Validate output
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validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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}
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TEST_SUITE_END() // W3x3
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|
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|
TEST_SUITE(W2x2) // GEMMDeconvolution
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerQuantizedFixture2x2<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(data2x2_precommit,
|
|
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
|
|
data_layouts_dataset),
|
|
framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 127), QuantizationInfo(2.f / 255.f, -128) })),
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|
framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, -10), QuantizationInfo(4.f / 255.f, 64) })),
|
|
add_bias_dataset))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
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|
}
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TEST_SUITE_END() // W2x2
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|
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|
TEST_SUITE(W1x1) // DirectDeconvolution and GEMMDeconvolution
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|
FIXTURE_DATA_TEST_CASE(Run, CLDeconvolutionLayerQuantizedFixture1x1<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(data1x1, framework::dataset::make("DataType",
|
|
DataType::QASYMM8_SIGNED)),
|
|
data_layouts_dataset),
|
|
framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1.f / 255.f, 0), QuantizationInfo(2.f / 255.f, 0) })),
|
|
framework::dataset::make("OutputQuantizationInfo", { QuantizationInfo(3.f / 255.f, 0), QuantizationInfo(4.f / 255.f, 0) })),
|
|
add_bias_dataset))
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_qasymm8, tolerance_num);
|
|
}
|
|
TEST_SUITE_END() // W1x1
|
|
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|
TEST_SUITE_END() // QASYMM8_SIGNED
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|
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|
TEST_SUITE_END() // Quantized
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|
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|
TEST_SUITE_END() // DeconvolutionLayer
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|
TEST_SUITE_END() // CL
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|
} // namespace validation
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|
} // namespace test
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|
} // namespace arm_compute
|