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/*
* Copyright (c) 2017-2020 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.
*/
#ifndef ARM_COMPUTE_TEST_SMALL_CONVOLUTION_LAYER_DATASET
#define ARM_COMPUTE_TEST_SMALL_CONVOLUTION_LAYER_DATASET
#include "tests/datasets/ConvolutionLayerDataset.h"
#include "utils/TypePrinter.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
namespace arm_compute
{
namespace test
{
namespace datasets
{
class SmallWinogradConvolutionLayer3x3Dataset final : public ConvolutionLayerDataset
{
public:
SmallWinogradConvolutionLayer3x3Dataset()
{
// Channel size big enough to force multithreaded execution of the input transform
add_config(TensorShape(8U, 8U, 32U), TensorShape(3U, 3U, 32U, 1U), TensorShape(1U), TensorShape(6U, 6U, 1U), PadStrideInfo(1, 1, 0, 0));
// Batch size 1
add_config(TensorShape(8U, 8U, 2U), TensorShape(3U, 3U, 2U, 1U), TensorShape(1U), TensorShape(6U, 6U, 1U), PadStrideInfo(1, 1, 0, 0));
// Batch size 4
add_config(TensorShape(23U, 27U, 5U, 4U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(21U, 25U, 21U, 4U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(8U, 8U, 2U), TensorShape(3U, 3U, 2U, 1U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 1, 1));
add_config(TensorShape(3U, 9U), TensorShape(3U, 3U), TensorShape(1), TensorShape(3U, 9U), PadStrideInfo(1, 1, 1, 1));
}
};
class SmallWinogradConvolutionLayer3x1Dataset final : public ConvolutionLayerDataset
{
public:
SmallWinogradConvolutionLayer3x1Dataset()
{
// Channel size big enough to force multithreaded execution of the input transform
add_config(TensorShape(8U, 8U, 32U), TensorShape(3U, 1U, 32U, 1U), TensorShape(1U), TensorShape(6U, 8U, 1U), PadStrideInfo(1, 1, 0, 0));
// Batch size 1
add_config(TensorShape(8U, 8U, 2U), TensorShape(3U, 1U, 2U, 1U), TensorShape(1U), TensorShape(6U, 8U, 1U), PadStrideInfo(1, 1, 0, 0));
// Batch size 4
add_config(TensorShape(23U, 27U, 5U, 4U), TensorShape(3U, 1U, 5U, 21U), TensorShape(21U), TensorShape(21U, 27U, 21U, 4U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(8U, 8U, 2U), TensorShape(3U, 1U, 2U, 1U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 1, 0));
}
};
class SmallWinogradConvolutionLayer1x3Dataset final : public ConvolutionLayerDataset
{
public:
SmallWinogradConvolutionLayer1x3Dataset()
{
// Channel size big enough to force multithreaded execution of the input transform
add_config(TensorShape(8U, 8U, 32U), TensorShape(1U, 3U, 32U, 1U), TensorShape(1U), TensorShape(8U, 6U, 1U), PadStrideInfo(1, 1, 0, 0));
// Batch size 1
add_config(TensorShape(8U, 8U, 2U), TensorShape(1U, 3U, 2U, 1U), TensorShape(1U), TensorShape(8U, 6U, 1U), PadStrideInfo(1, 1, 0, 0));
// Batch size 4
add_config(TensorShape(23U, 27U, 5U, 4U), TensorShape(1U, 3U, 5U, 21U), TensorShape(21U), TensorShape(23U, 25U, 21U, 4U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(8U, 8U, 2U), TensorShape(1U, 3U, 2U, 1U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 0, 1));
}
};
class SmallWinogradConvolutionLayer5x5Dataset final : public ConvolutionLayerDataset
{
public:
SmallWinogradConvolutionLayer5x5Dataset()
{
add_config(TensorShape(8U, 8U, 2U), TensorShape(5U, 5U, 2U, 1U), TensorShape(1U), TensorShape(4U, 4U, 1U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(8U, 8U, 2U), TensorShape(5U, 5U, 2U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 2, 2));
}
};
class SmallWinogradConvolutionLayer5x1Dataset final : public ConvolutionLayerDataset
{
public:
SmallWinogradConvolutionLayer5x1Dataset()
{
add_config(TensorShape(8U, 8U, 2U), TensorShape(5U, 1U, 2U, 1U), TensorShape(1U), TensorShape(4U, 8U, 1U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(8U, 8U, 2U), TensorShape(5U, 1U, 2U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 2, 0));
}
};
class SmallWinogradConvolutionLayer1x5Dataset final : public ConvolutionLayerDataset
{
public:
SmallWinogradConvolutionLayer1x5Dataset()
{
add_config(TensorShape(8U, 8U, 2U), TensorShape(1U, 5U, 2U, 1U), TensorShape(1U), TensorShape(8U, 4U, 1U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(8U, 8U, 2U), TensorShape(1U, 5U, 2U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 0, 2));
}
};
class SmallWinogradConvolutionLayer7x1Dataset final : public ConvolutionLayerDataset
{
public:
SmallWinogradConvolutionLayer7x1Dataset()
{
add_config(TensorShape(14U, 14U, 2U), TensorShape(7U, 1U, 2U, 1U), TensorShape(1U), TensorShape(8U, 14U, 1U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(14U, 14U, 2U), TensorShape(7U, 1U, 2U), TensorShape(1U), TensorShape(14U, 14U, 1U), PadStrideInfo(1, 1, 3, 0));
}
};
class SmallWinogradConvolutionLayer1x7Dataset final : public ConvolutionLayerDataset
{
public:
SmallWinogradConvolutionLayer1x7Dataset()
{
add_config(TensorShape(14U, 14U, 2U), TensorShape(1U, 7U, 2U, 1U), TensorShape(1U), TensorShape(14U, 8U, 1U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(14U, 14U, 2U), TensorShape(1U, 7U, 2U), TensorShape(1U), TensorShape(14U, 14U, 1U), PadStrideInfo(1, 1, 0, 3));
}
};
class SmallFFTConvolutionLayerDataset final : public ConvolutionLayerDataset
{
public:
SmallFFTConvolutionLayerDataset()
{
add_config(TensorShape(8U, 7U, 3U), TensorShape(3U, 3U, 3U, 2U), TensorShape(2U), TensorShape(8U, 7U, 2U), PadStrideInfo(1, 1, 1, 1));
add_config(TensorShape(64U, 32U, 5U), TensorShape(5U, 5U, 5U, 10U), TensorShape(10U), TensorShape(64U, 32U, 10U), PadStrideInfo(1, 1, 2, 2));
add_config(TensorShape(192U, 128U, 8U), TensorShape(9U, 9U, 8U, 3U), TensorShape(3U), TensorShape(192U, 128U, 3U), PadStrideInfo(1, 1, 4, 4));
}
};
class SmallConvolutionLayerDataset final : public ConvolutionLayerDataset
{
public:
SmallConvolutionLayerDataset()
{
// 1D Kernel
add_config(TensorShape(1U, 5U, 2U), TensorShape(1U, 3U, 2U, 3U), TensorShape(3U), TensorShape(1U, 7U, 3U), PadStrideInfo(1, 1, 0, 0, 2, 2, DimensionRoundingType::FLOOR));
// 1x1 Kernel with Stride (1, 1) and NHWC data layout in order to test skipping Im2Col
add_config(TensorShape(1U, 5U, 2U), TensorShape(1U, 1U, 2U, 3U), TensorShape(3U), TensorShape(1U, 5U, 3U), PadStrideInfo(1, 1, 0, 0));
// Batch size 1
add_config(TensorShape(23U, 27U, 5U), TensorShape(3U, 3U, 5U, 2U), TensorShape(2U), TensorShape(11U, 25U, 2U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 7U), TensorShape(5U, 5U, 7U, 3U), TensorShape(3U), TensorShape(11U, 12U, 3U), PadStrideInfo(3, 2, 1, 0));
add_config(TensorShape(17U, 31U, 2U), TensorShape(5U, 5U, 2U, 4U), TensorShape(4U), TensorShape(15U, 15U, 4U), PadStrideInfo(1, 2, 1, 1));
add_config(TensorShape(3U, 3U, 1U), TensorShape(2U, 2U, 1U, 5U), TensorShape(5U), TensorShape(2U, 2U, 5U), PadStrideInfo(1, 1, 0, 0));
// Batch size different than one
add_config(TensorShape(23U, 27U, 5U, 4U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(11U, 25U, 21U, 4U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 7U, 4U), TensorShape(5U, 5U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 4U), PadStrideInfo(3, 2, 1, 0));
add_config(TensorShape(17U, 31U, 2U, 4U), TensorShape(5U, 5U, 2U, 19U), TensorShape(19U), TensorShape(15U, 15U, 19U, 4U), PadStrideInfo(1, 2, 1, 1));
// FC convolution
add_config(TensorShape(1U, 1U, 1024U), TensorShape(1U, 1U, 1024U, 1001U), TensorShape(1001U), TensorShape(1U, 1U, 1001U), PadStrideInfo(1, 1, 0, 0));
// Asymmetric padding
add_config(TensorShape(33U, 27U, 3U), TensorShape(5U, 7U, 3U, 4U), TensorShape(4U), TensorShape(11U, 12U, 4U), PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 3U), TensorShape(5U, 7U, 3U, 4U), TensorShape(4U), TensorShape(11U, 12U, 4U), PadStrideInfo(3, 2, 1, 1, 0, 2, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 3U), TensorShape(5U, 7U, 3U, 4U), TensorShape(4U), TensorShape(11U, 12U, 4U), PadStrideInfo(3, 2, 2, 1, 2, 0, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 3U), TensorShape(5U, 7U, 3U, 4U), TensorShape(4U), TensorShape(11U, 12U, 4U), PadStrideInfo(3, 2, 1, 3, 0, 2, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 3U), TensorShape(5U, 7U, 3U, 4U), TensorShape(4U), TensorShape(10U, 11U, 4U), PadStrideInfo(3, 2, 1, 0, 1, 0, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 3U), TensorShape(5U, 7U, 3U, 4U), TensorShape(4U), TensorShape(10U, 11U, 4U), PadStrideInfo(3, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR));
add_config(TensorShape(5U, 4U, 3U, 2U), TensorShape(4U, 4U, 3U, 1U), TensorShape(1U), TensorShape(2U, 1U, 1U, 2U), PadStrideInfo(1, 1, 0, 0, 0, 0, DimensionRoundingType::FLOOR));
}
};
// TODO (COMPMID-1749)
class SmallConvolutionLayerReducedDataset final : public ConvolutionLayerDataset
{
public:
SmallConvolutionLayerReducedDataset()
{
// Batch size 1
add_config(TensorShape(23U, 27U, 5U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(11U, 25U, 21U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 7U), TensorShape(5U, 5U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U), PadStrideInfo(3, 2, 1, 0));
add_config(TensorShape(17U, 31U, 2U), TensorShape(5U, 5U, 2U, 19U), TensorShape(19U), TensorShape(15U, 15U, 19U), PadStrideInfo(1, 2, 1, 1));
// Asymmetric padding
add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 1, 1, 0, 2, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 2, 1, 2, 0, DimensionRoundingType::FLOOR));
}
};
class SmallGroupedConvolutionLayerDataset final : public ConvolutionLayerDataset
{
public:
SmallGroupedConvolutionLayerDataset()
{
// Batch size 1
// Number of groups = 2
add_config(TensorShape(23U, 27U, 8U), TensorShape(1U, 1U, 4U, 24U), TensorShape(24U), TensorShape(12U, 27U, 24U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 12U), TensorShape(5U, 5U, 6U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U), PadStrideInfo(3, 2, 1, 0));
// Number of groups = 4
add_config(TensorShape(23U, 27U, 8U), TensorShape(1U, 1U, 2U, 24U), TensorShape(24U), TensorShape(12U, 27U, 24U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 12U), TensorShape(5U, 5U, 4U, 15U), TensorShape(15U), TensorShape(11U, 12U, 15U), PadStrideInfo(3, 2, 1, 0));
// Batch size 4
// Number of groups = 2
add_config(TensorShape(23U, 27U, 8U, 4U), TensorShape(1U, 1U, 4U, 24U), TensorShape(24U), TensorShape(12U, 27U, 24U, 4U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 12U, 4U), TensorShape(5U, 5U, 6U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 4U), PadStrideInfo(3, 2, 1, 0));
// Number of groups = 4
add_config(TensorShape(23U, 27U, 8U, 4U), TensorShape(1U, 1U, 2U, 24U), TensorShape(24U), TensorShape(12U, 27U, 24U, 4U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 12U, 4U), TensorShape(5U, 5U, 4U, 15U), TensorShape(15U), TensorShape(11U, 12U, 15U, 4U), PadStrideInfo(3, 2, 1, 0));
// Arbitrary batch size
add_config(TensorShape(23U, 27U, 8U, 5U), TensorShape(1U, 1U, 4U, 24U), TensorShape(24U), TensorShape(12U, 27U, 24U, 5U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 12U, 3U), TensorShape(5U, 5U, 6U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 3U), PadStrideInfo(3, 2, 1, 0));
// Number of groups = 4
add_config(TensorShape(23U, 27U, 8U, 2U), TensorShape(1U, 1U, 2U, 24U), TensorShape(24U), TensorShape(12U, 27U, 24U, 2U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 12U, 5U), TensorShape(5U, 5U, 4U, 15U), TensorShape(15U), TensorShape(11U, 12U, 15U, 5U), PadStrideInfo(3, 2, 1, 0));
// Asymmetric padding
add_config(TensorShape(33U, 27U, 8U, 5U), TensorShape(5U, 7U, 2U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 8U, 5U), TensorShape(5U, 7U, 4U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 1, 1, 0, 2, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 6U, 5U), TensorShape(5U, 7U, 3U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 2, 1, 2, 0, DimensionRoundingType::FLOOR));
}
};
} // namespace datasets
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_SMALL_CONVOLUTION_LAYER_DATASET */
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