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301 lines
24 KiB
301 lines
24 KiB
/*
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* Copyright (c) 2021, 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/runtime/CL/CLTensor.h"
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#include "arm_compute/runtime/CL/functions/CLConv3D.h"
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#include "arm_compute/runtime/FunctionDescriptors.h"
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#include "tests/CL/CLAccessor.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/DirectConvolution3DFixture.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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const RelativeTolerance<half> rel_tolerance_fp16(half(0.2)); /**< Relative tolerance for FP16 tests */
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constexpr float abs_tolerance_fp16(0.02f); /**< Absolute tolerance for FP16 tests */
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constexpr RelativeTolerance<float> rel_tolerance_fp32(0.05f); /**< Relative tolerance for FP32 tests */
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constexpr float abs_tolerance_fp32(0.0001f); /**< Absolute tolerance for FP32 tests*/
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constexpr AbsoluteTolerance<uint8_t> abs_tolerance_qasymm8(1); /**< Absolute tolerance for quantized 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(DirectConvolution3D)
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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(zip(zip(
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framework::dataset::make("InputShape", { TensorShape(27U, 13U, 5U, 3U), // Unsupported data layout
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TensorShape(27U, 13U, 5U, 3U), // Unsupported activation enabled
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TensorShape(27U, 13U, 5U, 3U), // Mismatching data type
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TensorShape(27U, 13U, 5U, 3U), // Unsupported data type
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TensorShape(27U, 13U, 5U, 3U), // Mismatching input feature maps
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TensorShape(27U, 13U, 5U, 3U), // Mismatching output feature maps
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TensorShape(27U, 13U, 5U, 3U), // Mismatching bias shape
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TensorShape(27U, 13U, 5U, 3U), // Unsupported number of weights dimensions
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TensorShape(27U, 13U, 5U, 3U), // Unsupported number of biases dimensions
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TensorShape(27U, 13U, 5U, 3U), // Mismatching output shape
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TensorShape(27U, 13U, 5U, 3U)
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}),
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framework::dataset::make("WeightsShape", { TensorShape(4U, 27U, 3U, 3U, 3U),
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TensorShape(4U, 27U, 3U, 3U, 3U),
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TensorShape(4U, 27U, 3U, 3U, 3U),
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TensorShape(4U, 27U, 3U, 3U, 3U),
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TensorShape(4U, 32U, 3U, 3U, 3U),
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TensorShape(8U, 27U, 3U, 3U, 3U),
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TensorShape(4U, 27U, 3U, 3U, 3U),
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TensorShape(4U, 27U, 3U, 3U, 3U, 2U),
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TensorShape(4U, 27U, 3U, 3U, 3U),
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TensorShape(4U, 27U, 3U, 3U, 3U),
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TensorShape(4U, 27U, 3U, 3U, 3U)
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})),
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framework::dataset::make("BiasesShape", { TensorShape(4U),
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TensorShape(4U),
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TensorShape(4U),
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TensorShape(4U),
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TensorShape(4U),
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TensorShape(4U),
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TensorShape(8U),
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TensorShape(4U),
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TensorShape(4U),
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TensorShape(4U),
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TensorShape(4U)
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})),
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framework::dataset::make("OutputShape", { TensorShape(4U, 13U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U, 2U),
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TensorShape(4U, 11U, 5U, 3U),
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TensorShape(4U, 13U, 5U, 3U)
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})),
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framework::dataset::make("Conv3dInfo", { Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false),
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Conv3dInfo(Size3D(1U, 1U, 1U), Padding3D(1U, 1U, 1U), ActivationLayerInfo(), Size3D(1U, 1U, 1U), DimensionRoundingType::FLOOR, false)
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})),
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framework::dataset::make("SrcDataType", { DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::U32,
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DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::F32
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})),
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framework::dataset::make("WeightsDataType", { DataType::F32,
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DataType::F32,
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DataType::F16,
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DataType::U32,
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DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::F32,
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DataType::F32
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})),
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framework::dataset::make("DataLayout", { DataLayout::NCDHW,
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DataLayout::NDHWC,
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DataLayout::NDHWC,
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DataLayout::NDHWC,
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DataLayout::NDHWC,
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DataLayout::NDHWC,
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DataLayout::NDHWC,
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DataLayout::NDHWC,
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DataLayout::NDHWC,
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DataLayout::NDHWC,
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DataLayout::NDHWC
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})),
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framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, true })),
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input_shape, weights_shape, biases_shape, output_shape, conv3d_info, src_data_type, weights_data_type, data_layout, expected)
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{
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TensorInfo input_info = TensorInfo(input_shape, 1, src_data_type);
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TensorInfo weights_info = TensorInfo(weights_shape, 1, weights_data_type);
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TensorInfo biases_info = TensorInfo(biases_shape, 1, src_data_type);
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TensorInfo output_info = TensorInfo(output_shape, 1, src_data_type);
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input_info.set_data_layout(data_layout);
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weights_info.set_data_layout(data_layout);
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biases_info.set_data_layout(data_layout);
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output_info.set_data_layout(data_layout);
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bool is_valid = bool(CLConv3D::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), conv3d_info));
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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 CLDirectConvolution3DFixture = DirectConvolution3DValidationFixture<CLTensor, CLAccessor, CLConv3D, T>;
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template <typename T>
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using CLDirectConvolution3DQuantizedFixture = DirectConvolution3DValidationQuantizedFixture<CLTensor, CLAccessor, CLConv3D, T>;
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TEST_SUITE(NDHWC)
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TEST_SUITE(FP16)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolution3DFixture<half>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputShape", { TensorShape(7U, 5U, 3U, 13U, 3U),
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TensorShape(15U, 7U, 11U, 7U),
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TensorShape(19U, 5U, 16U, 4U),
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TensorShape(13U, 5U, 17U, 2U)
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}),
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framework::dataset::make("StrideX", { 1, 3, 2, 1 })),
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framework::dataset::make("StrideY", { 2, 1, 3, 1 })),
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framework::dataset::make("StrideZ", { 3, 2, 1, 1 })),
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framework::dataset::make("PadX", { 0, 2, 1, 0 })),
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framework::dataset::make("PadY", { 1, 0, 2, 0 })),
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framework::dataset::make("PadZ", { 2, 1, 0, 0 })),
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framework::dataset::make("KernelWidth", { 3, 7, 5, 1 })),
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framework::dataset::make("KernelHeight", { 5, 3, 7, 1 })),
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framework::dataset::make("KernelDepth", { 7, 5, 3, 1 })),
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framework::dataset::make("NumKernels", { 5, 3, 1, 11 })),
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framework::dataset::make("HasBias", { true, true, true, false })),
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framework::dataset::make("Activation", ActivationLayerInfo())),
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framework::dataset::make("DataType", DataType::F16)),
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framework::dataset::make("DataLayout", DataLayout::NDHWC)))
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{
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validate(CLAccessor(_target), _reference, rel_tolerance_fp16, tolerance_num, abs_tolerance_fp16);
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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, CLDirectConvolution3DFixture<float>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputShape", { TensorShape(7U, 5U, 3U, 13U, 3U),
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TensorShape(15U, 7U, 11U, 7U),
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TensorShape(19U, 5U, 16U, 4U),
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TensorShape(13U, 5U, 17U, 2U)
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}),
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framework::dataset::make("StrideX", { 1, 3, 2, 1 })),
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framework::dataset::make("StrideY", { 2, 1, 3, 1 })),
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framework::dataset::make("StrideZ", { 3, 2, 1, 1 })),
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framework::dataset::make("PadX", { 0, 2, 1, 0 })),
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framework::dataset::make("PadY", { 1, 0, 2, 0 })),
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framework::dataset::make("PadZ", { 2, 1, 0, 0 })),
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framework::dataset::make("KernelWidth", { 3, 7, 5, 1 })),
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framework::dataset::make("KernelHeight", { 5, 3, 7, 1 })),
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framework::dataset::make("KernelDepth", { 7, 5, 3, 1 })),
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framework::dataset::make("NumKernels", { 5, 3, 1, 11 })),
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framework::dataset::make("HasBias", { true, true, true, false })),
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framework::dataset::make("Activation", ActivationLayerInfo())),
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framework::dataset::make("DataType", DataType::F32)),
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framework::dataset::make("DataLayout", DataLayout::NDHWC)))
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{
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validate(CLAccessor(_target), _reference, rel_tolerance_fp32, 0.0, abs_tolerance_fp32);
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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(QASYMM8)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolution3DQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputShape", { TensorShape(7U, 5U, 3U, 13U, 3U),
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TensorShape(15U, 7U, 11U, 7U),
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TensorShape(19U, 5U, 16U, 4U),
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TensorShape(13U, 5U, 17U, 2U)
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}),
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framework::dataset::make("StrideX", { 1, 3, 2, 1 })),
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framework::dataset::make("StrideY", { 2, 1, 3, 1 })),
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framework::dataset::make("StrideZ", { 3, 2, 1, 1 })),
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framework::dataset::make("PadX", { 0, 2, 1, 0 })),
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framework::dataset::make("PadY", { 1, 0, 2, 0 })),
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framework::dataset::make("PadZ", { 2, 1, 0, 0 })),
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framework::dataset::make("KernelWidth", { 3, 7, 5, 1 })),
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framework::dataset::make("KernelHeight", { 5, 3, 7, 1 })),
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framework::dataset::make("KernelDepth", { 7, 5, 3, 1 })),
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framework::dataset::make("NumKernels", { 5, 3, 1, 11 })),
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framework::dataset::make("HasBias", { true, true, true, false })),
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framework::dataset::make("Activation", ActivationLayerInfo())),
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framework::dataset::make("DataType", DataType::QASYMM8)),
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framework::dataset::make("DataLayout", DataLayout::NDHWC)),
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framework::dataset::make("SrcQuantizationInfo", QuantizationInfo(0.1f, 10))),
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framework::dataset::make("WeightsQuantizationInfo", QuantizationInfo(0.3f, 20))),
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framework::dataset::make("DstQuantizationInfo", QuantizationInfo(0.2f, 5))))
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{
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validate(CLAccessor(_target), _reference, abs_tolerance_qasymm8);
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}
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TEST_SUITE_END() // QASYMM8
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TEST_SUITE(QASYMM8_SIGNED)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolution3DQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputShape", { TensorShape(7U, 5U, 3U, 13U, 3U),
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TensorShape(15U, 7U, 11U, 7U),
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TensorShape(19U, 5U, 16U, 4U),
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TensorShape(13U, 5U, 17U, 2U)
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}),
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framework::dataset::make("StrideX", { 1, 3, 2, 1 })),
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framework::dataset::make("StrideY", { 2, 1, 3, 1 })),
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framework::dataset::make("StrideZ", { 3, 2, 1, 1 })),
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framework::dataset::make("PadX", { 0, 2, 1, 0 })),
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framework::dataset::make("PadY", { 1, 0, 2, 0 })),
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framework::dataset::make("PadZ", { 2, 1, 0, 0 })),
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framework::dataset::make("KernelWidth", { 3, 7, 5, 1 })),
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framework::dataset::make("KernelHeight", { 5, 3, 7, 1 })),
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framework::dataset::make("KernelDepth", { 7, 5, 3, 1 })),
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framework::dataset::make("NumKernels", { 5, 3, 1, 11 })),
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framework::dataset::make("HasBias", { true, true, true, false })),
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framework::dataset::make("Activation", ActivationLayerInfo())),
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framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
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framework::dataset::make("DataLayout", DataLayout::NDHWC)),
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framework::dataset::make("SrcQuantizationInfo", QuantizationInfo(0.1f, 10))),
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framework::dataset::make("WeightsQuantizationInfo", QuantizationInfo(0.3f, 20))),
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framework::dataset::make("DstQuantizationInfo", QuantizationInfo(0.2f, 5))))
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{
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validate(CLAccessor(_target), _reference, abs_tolerance_qasymm8);
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}
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TEST_SUITE_END() // QASYMM8_SIGNED
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TEST_SUITE_END() // NDHWC
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TEST_SUITE_END() // DirectConvolution3D
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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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