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252 lines
17 KiB
252 lines
17 KiB
/*
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* Copyright (c) 2022-2023 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 "tests/AssetsLibrary.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/framework/Fixture.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/reference/ConvolutionLayer.h"
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#include "tests/datasets/SmallConvolutionLayerDataset.h"
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#include "tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.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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/** Tolerances from tests/validation/CL/DirectConvolutionLayer.cpp
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*/
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RelativeTolerance<float> tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
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RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
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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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} // namespace
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TEST_SUITE(CL)
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TEST_SUITE(DYNAMIC_FUSION)
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/** Synced with tests/validation/CL/ConvolutionLayer.cpp
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*
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* Difference | Why the difference
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* f32 tolerance here is smaller | To use the same tolerance as that of DirectConv2d; lowering tolerance is safe
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* No quantized tests | Not supported yet
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* No grouped CNN tests | Not supported yet
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* No mixed layout tests | Not needed; only NHWC is supported
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* No activation/post op tests | Not needed in fusion
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* No ValidateConvolutionMethod | Only a single method (direct conv2d) is supported
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* No ReshapeWeights = true tests | Not applicable yet. This parameter only concerns gemm-based conv2d
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* No RunSmallWithPadding tests | Padding is removed
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*
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*/
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TEST_SUITE(CONV2D)
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template <typename T>
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using DynamicFusionGpuConv2dFixture = DynamicFusionGpuConv2dValidationFixture<CLTensor, CLAccessor, GpuConv2d, T>;
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TEST_SUITE(FP32)
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FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
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framework::dataset::make("DataType", DataType::F32)),
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framework::dataset::make("DataLayout", { DataLayout::NHWC })),
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framework::dataset::make("QuantizationInfo", QuantizationInfo())))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_f32);
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}
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TEST_SUITE_END() // FP32
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TEST_SUITE(FP16)
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FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
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framework::dataset::make("DataType", DataType::F16)),
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framework::dataset::make("DataLayout", { DataLayout::NHWC })),
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framework::dataset::make("QuantizationInfo", QuantizationInfo())))
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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() // FP16
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// Tests for specific conv2d methods
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/** Synced with tests/validation/CL/DirectConvolutionLayer.cpp
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*
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* Difference | Why the difference
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* No quantized tests | Not supported yet
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* No Invalid output size test | Not applicable. Output is removed from the interface
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* No mixed layout/NCHW tests | Not needed; only NHWC is supported
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* No activation tests | Not needed in fusion
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*/
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TEST_SUITE(DIRECT_CONV2D)
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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(
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framework::dataset::make("InputInfo", { TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid: Mismatching data type input/weights
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TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid: Mismatching input feature maps
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TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid weights dimensions
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TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Unsupported biases size
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TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Unsupported biases dimensions
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Unsupported data layout: NCHW
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TensorInfo(TensorShape(2U, 32U, 16U), 1, DataType::QASYMM8, DataLayout::NHWC), // Unsupported data type: quantized
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TensorInfo(TensorShape(2U, 32U, 16U), 1, DataType::F32, DataLayout::NHWC),
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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",{ TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F16, DataLayout::NHWC),
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TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(2U, 3U, 3U, 4U, 3U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(2U, 3U, 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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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW),
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TensorInfo(TensorShape(2U, 1U, 1U, 4U), 1, DataType::QASYMM8, DataLayout::NHWC),
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TensorInfo(TensorShape(2U, 1U, 1U, 4U), 1, DataType::F32, DataLayout::NHWC),
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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",{ 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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TensorInfo(TensorShape(3U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, DataLayout::NHWC),
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TensorInfo(TensorShape(25U), 1, DataType::F32, DataLayout::NCHW),
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TensorInfo(TensorShape(4U), 1, DataType::QASYMM8, 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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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("Conv2dAttributes", {
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
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Conv2dAttributes().stride({3, 3}).pad({0, 0, 0, 0}),
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})),
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framework::dataset::make("Expected", { false, false, false, false, false, false, false, true, true, true, true })),
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input_info, weights_info, biases_info, conv2d_attrs, expected)
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{
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auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
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auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx };
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GpuWorkloadSketch sketch{ &gpu_ctx };
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const TensorInfo sketch_input_info = sketch.create_tensor_info(input_info);
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const TensorInfo sketch_weights_info = sketch.create_tensor_info(weights_info);
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const TensorInfo sketch_biases_info = sketch.create_tensor_info(biases_info);
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bool is_valid = bool(GpuConv2d::validate_op(sketch, &sketch_input_info, &sketch_weights_info, &sketch_biases_info, conv2d_attrs));
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ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
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}
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template <typename T>
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using DynamicFusionGpuDirectConv2dFixture = DynamicFusionDirectConv2dValidationFixture<CLTensor, CLAccessor, GpuConv2d, T>;
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TEST_SUITE(FP16)
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FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDirectConv2dFixture<half>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(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("DataLayout", DataLayout::NHWC)))
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{
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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, DynamicFusionGpuDirectConv2dFixture<half>, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(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("DataLayout", DataLayout::NHWC)))
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{
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validate(CLAccessor(_target), _reference, tolerance_f16, 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, DynamicFusionGpuDirectConv2dFixture<float>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(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::F32)),
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framework::dataset::make("DataLayout", DataLayout::NHWC)))
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{
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validate(CLAccessor(_target), _reference, tolerance_f32, 0.0, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDirectConv2dFixture<float>, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(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::F32)),
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framework::dataset::make("DataLayout", DataLayout::NHWC)))
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{
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validate(CLAccessor(_target), _reference, tolerance_f32, 0.0, abs_tolerance_f32);
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}
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// clang-format on
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// *INDENT-ON*
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TEST_SUITE_END() // FP32
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TEST_SUITE_END() // DIRECT_CONV2D
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TEST_SUITE_END() // CONV2D
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TEST_SUITE_END() // DYNAMIC_FUSION
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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
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