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/*
* Copyright (c) 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_SCALE_VALIDATION_DATASET
#define ARM_COMPUTE_TEST_SCALE_VALIDATION_DATASET
#include "utils/TypePrinter.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "tests/datasets/BorderModeDataset.h"
#include "tests/datasets/InterpolationPolicyDataset.h"
#include "tests/datasets/SamplingPolicyDataset.h"
#include "tests/datasets/ShapeDatasets.h"
namespace arm_compute
{
namespace test
{
namespace datasets
{
/** Class to generate boundary values for the given template parameters
* including shapes with large differences between width and height.
* element_per_iteration is the number of elements processed by one iteration
* of an implementation. (E.g., if an iteration is based on a 16-byte vector
* and size of one element is 1-byte, this value would be 16.).
* iterations is the total number of complete iterations we want to test
* for the effect of larger shapes.
*/
template <uint32_t channel, uint32_t batch, uint32_t element_per_iteration, uint32_t iterations>
class ScaleShapesBaseDataSet : public ShapeDataset
{
static constexpr auto boundary_minus_one = element_per_iteration * iterations - 1;
static constexpr auto boundary_plus_one = element_per_iteration * iterations + 1;
static constexpr auto small_size = 3;
public:
// These tensor shapes are NCHW layout, fixture will convert to NHWC.
ScaleShapesBaseDataSet()
: ShapeDataset("Shape",
{
TensorShape{ small_size, boundary_minus_one, channel, batch },
TensorShape{ small_size, boundary_plus_one, channel, batch },
TensorShape{ boundary_minus_one, small_size, channel, batch },
TensorShape{ boundary_plus_one, small_size, channel, batch },
TensorShape{ boundary_minus_one, boundary_plus_one, channel, batch },
TensorShape{ boundary_plus_one, boundary_minus_one, channel, batch },
})
{
}
};
/** For the single vector, only larger value (+1) than boundary
* since smaller value (-1) could cause some invalid shapes like
* - invalid zero size
* - size 1 which isn't compatible with scale with aligned corners.
*/
template <uint32_t channel, uint32_t batch, uint32_t element_per_iteration>
class ScaleShapesBaseDataSet<channel, batch, element_per_iteration, 1> : public ShapeDataset
{
static constexpr auto small_size = 3;
static constexpr auto boundary_plus_one = element_per_iteration + 1;
public:
// These tensor shapes are NCHW layout, fixture will convert to NHWC.
ScaleShapesBaseDataSet()
: ShapeDataset("Shape",
{
TensorShape{ small_size, boundary_plus_one, channel, batch },
TensorShape{ boundary_plus_one, small_size, channel, batch },
})
{
}
};
/** For the shapes smaller than one vector, only pre-defined tiny shapes
* are tested (3x2, 2x3) as smaller shapes are more likely to cause
* issues and easier to debug.
*/
template <uint32_t channel, uint32_t batch, uint32_t element_per_iteration>
class ScaleShapesBaseDataSet<channel, batch, element_per_iteration, 0> : public ShapeDataset
{
static constexpr auto small_size = 3;
static constexpr auto zero_vector_boundary_value = 2;
public:
// These tensor shapes are NCHW layout, fixture will convert to NHWC.
ScaleShapesBaseDataSet()
: ShapeDataset("Shape",
{
TensorShape{ small_size, zero_vector_boundary_value, channel, batch },
TensorShape{ zero_vector_boundary_value, small_size, channel, batch },
})
{
}
};
/** Interpolation policy test set */
const auto ScaleInterpolationPolicySet = framework::dataset::make("InterpolationPolicy",
{
InterpolationPolicy::NEAREST_NEIGHBOR,
InterpolationPolicy::BILINEAR,
});
/** Scale data types */
const auto ScaleDataLayouts = framework::dataset::make("DataLayout",
{
DataLayout::NCHW,
DataLayout::NHWC,
});
/** Sampling policy data set */
const auto ScaleSamplingPolicySet = combine(datasets::SamplingPolicies(),
framework::dataset::make("AlignCorners", { false }));
/** Sampling policy data set for Aligned Corners which only allows TOP_LEFT policy.*/
const auto ScaleAlignCornersSamplingPolicySet = combine(framework::dataset::make("SamplingPolicy",
{
SamplingPolicy::TOP_LEFT,
}),
framework::dataset::make("AlignCorners", { true }));
/** Generated shapes: Used by NEON precommit and nightly
* - 2D shapes with 0, 1, 2 vector iterations
* - 3D shapes with 0, 1 vector iterations
* - 4D shapes with 0 vector iterations
*/
#define SCALE_SHAPE_DATASET(element_per_iteration) \
concat(concat(concat(concat(concat(ScaleShapesBaseDataSet<1, 1, (element_per_iteration), 0>(), \
ScaleShapesBaseDataSet<1, 1, (element_per_iteration), 1>()), \
ScaleShapesBaseDataSet<1, 1, (element_per_iteration), 2>()), \
ScaleShapesBaseDataSet<3, 1, (element_per_iteration), 0>()), \
ScaleShapesBaseDataSet<3, 1, (element_per_iteration), 1>()), \
ScaleShapesBaseDataSet<3, 3, (element_per_iteration), 0>())
// To prevent long precommit time for OpenCL, shape set for OpenCL is separated into below two parts.
/** Generated shapes for precommits to achieve essential coverage. Used by CL precommit and nightly
* - 3D shapes with 1 vector iterations
* - 4D shapes with 1 vector iterations
*/
#define SCALE_PRECOMMIT_SHAPE_DATASET(element_per_iteration) \
concat(ScaleShapesBaseDataSet<3, 1, (element_per_iteration), 1>(), ScaleShapesBaseDataSet<3, 3, (element_per_iteration), 1>())
/** Generated shapes for nightly to achieve more small and variety shapes. Used by CL nightly
* - 2D shapes with 0, 1, 2 vector iterations
* - 3D shapes with 0 vector iterations (1 vector iteration is covered by SCALE_PRECOMMIT_SHAPE_DATASET)
* - 4D shapes with 0 vector iterations
*/
#define SCALE_NIGHTLY_SHAPE_DATASET(element_per_iteration) \
concat(concat(concat(concat(ScaleShapesBaseDataSet<1, 1, (element_per_iteration), 0>(), \
ScaleShapesBaseDataSet<1, 1, (element_per_iteration), 1>()), \
ScaleShapesBaseDataSet<1, 1, (element_per_iteration), 2>()), \
ScaleShapesBaseDataSet<3, 1, (element_per_iteration), 0>()), \
ScaleShapesBaseDataSet<3, 3, (element_per_iteration), 0>())
/** Generating dataset for non-quantized data tyeps with the given shapes */
#define ASSEMBLE_DATASET(shape, samping_policy_set) \
combine(combine(combine(combine((shape), ScaleDataLayouts), \
ScaleInterpolationPolicySet), \
datasets::BorderModes()), \
samping_policy_set)
/** Generating dataset for quantized data tyeps with the given shapes */
#define ASSEMBLE_QUANTIZED_DATASET(shape, sampling_policy_set, quantization_info_set) \
combine(combine(combine(combine(combine(shape, \
quantization_info_set), \
ScaleDataLayouts), \
ScaleInterpolationPolicySet), \
datasets::BorderModes()), \
sampling_policy_set)
} // namespace datasets
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_SCALE_VALIDATION_DATASET */
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