tests/validation/CL/DirectConvolutionLayer.cpp

/*
 * Copyright (c) 2017-2018 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/CLTensorAllocator.h"
#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
#include "tests/CL/CLAccessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/DirectConvolutionLayerDataset.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/DirectConvolutionLayerFixture.h"

namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
// COMPMID-517 Investigate the mismatch to see whether it is a real bug
RelativeTolerance<half>              tolerance_fp16(half(0.2)); /**< Tolerance for floating point tests */
RelativeTolerance<float>             tolerance_fp32(0.02f);     /**< Tolerance for floating point tests */
constexpr float                      tolerance_num = 0.07f;     /**< Tolerance number */
constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1);      /**< Tolerance for quantized tests */

const auto data_strides     = combine(framework::dataset::make("StrideX", 1, 3), framework::dataset::make("StrideY", 1, 3));
const auto data_ksize_one   = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 1)));
const auto data_ksize_three = combine(framework::dataset::make("PadX", 0, 2), combine(framework::dataset::make("PadY", 0, 2), framework::dataset::make("KernelSize", 3)));
const auto data_ksize_five  = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 5)));
const auto data_all_kernels = concat(concat(data_ksize_one, data_ksize_three), data_ksize_five);

const auto data = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_all_kernels));

/** Direct convolution nightly data set. */
const auto data_nightly = combine(data, framework::dataset::make("NumKernels", { 1, 4, 8, 16 }));
/** Direct convolution precommit data set. */
const auto data_precommit = combine(data, framework::dataset::make("NumKernels", { 4 }));

/** Activation function Dataset*/
const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
{
    ActivationLayerInfo(),
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f),
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
});
} // namespace

TEST_SUITE(CL)
TEST_SUITE(DirectConvolutionLayer)

//TODO(COMPMID-415): Configuration tests?

// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
               framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching input feature maps
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported kernel width
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non-rectangular weights dimensions
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid stride
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
                                                       TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
                                                     }),
               framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
                                                        TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
                                                        TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
                                                     })),
               framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(3U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U, 2U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(4U), 1, DataType::F32),
                                                     })),
               framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
                                                     })),
               framework::dataset::make("ConvInfo",  { PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(3, 3, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                       PadStrideInfo(1, 1, 0, 0),
                                                      })),
                       framework::dataset::make("ActivationInfo",
{
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)
})),
               framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, true })),
               input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
{
    bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*

template <typename T>
using CLDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
template <typename T>
using CLDirectConvolutionValidationWithTensorShapesFixture = DirectConvolutionValidationWithTensorShapesFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;

TEST_SUITE(Float)
TEST_SUITE(FP16)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType", DataType::F16)),
                                                                                                                   ActivationFunctionsDataset),
                                                                                                                   framework::dataset::make("DataLayout", DataLayout::NCHW)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType", DataType::F16)),
                                                                                                                 ActivationFunctionsDataset),
                                                                                                                 framework::dataset::make("DataLayout", DataLayout::NCHW)))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
}
TEST_SUITE_END() // FP16

TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
                                                                                                                    DataType::F32)),
                                                                                                                    ActivationFunctionsDataset),
                                                                                                                    framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
    validate(CLAccessor(_target), _reference, tolerance_fp32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType", DataType::F32)),
                                                                                                                  ActivationFunctionsDataset),
                                                                                                                  framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
    validate(CLAccessor(_target), _reference, tolerance_fp32);
}
TEST_SUITE_END() // FP32

TEST_SUITE(FP32_CustomDataset)
FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesFixture<float>, framework::DatasetMode::ALL, combine(combine(datasets::DirectConvolutionLayerDataset(),
                       framework::dataset::make("DataType", DataType::F32)),
                       ActivationFunctionsDataset))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_fp32);
}
TEST_SUITE_END() // FP32_CustomDataset
TEST_SUITE_END() // Float

template <typename T>
using CLDirectConvolutionLayerQuantizedFixture = DirectConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
template <typename T>
using CLDirectConvolutionValidationWithTensorShapesQuantizedFixture = DirectConvolutionValidationWithTensorShapesQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;

const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
{
    ActivationLayerInfo(),
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
});
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
                       DataType::QASYMM8)),
                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10) })),
                       QuantizedActivationFunctionsDataset))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType",
                       DataType::QASYMM8)),
                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10) })),
                       QuantizedActivationFunctionsDataset))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
TEST_SUITE_END() // QASYMM8

TEST_SUITE(QASYMM8_CustomDataset)
FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::DirectConvolutionLayerDataset(),
                       framework::dataset::make("DataType", DataType::QASYMM8)),
                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127) })),
                       QuantizedActivationFunctionsDataset))
{
    // Validate output
    validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
TEST_SUITE_END() // QASYMM8_CustomDataset
TEST_SUITE_END() // Quantized

TEST_SUITE_END() // DirectConvolutionLayer
TEST_SUITE_END() // Float
} // namespace validation
} // namespace test
} // namespace arm_compute