467 lines
22 KiB
C++
467 lines
22 KiB
C++
/*
|
|
* 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.
|
|
*/
|
|
#include "arm_compute/core/Helpers.h"
|
|
#include "arm_compute/core/Types.h"
|
|
#include "arm_compute/runtime/NEON/functions/NEScale.h"
|
|
#include "arm_compute/runtime/Tensor.h"
|
|
#include "arm_compute/runtime/TensorAllocator.h"
|
|
#include "tests/NEON/Accessor.h"
|
|
#include "tests/PaddingCalculator.h"
|
|
#include "tests/datasets/ScaleValidationDataset.h"
|
|
#include "tests/framework/Asserts.h"
|
|
#include "tests/framework/Macros.h"
|
|
#include "tests/validation/Helpers.h"
|
|
#include "tests/validation/Validation.h"
|
|
#include "tests/validation/fixtures/ScaleFixture.h"
|
|
|
|
namespace arm_compute
|
|
{
|
|
namespace test
|
|
{
|
|
namespace validation
|
|
{
|
|
namespace
|
|
{
|
|
using datasets::ScaleShapesBaseDataSet;
|
|
using datasets::ScaleInterpolationPolicySet;
|
|
using datasets::ScaleDataLayouts;
|
|
using datasets::ScaleSamplingPolicySet;
|
|
using datasets::ScaleAlignCornersSamplingPolicySet;
|
|
|
|
/** We consider vector size in byte 64 since the maximum size of
|
|
* a vector used by @ref NEScaleKernel is currently 64-byte (float32x4x4).
|
|
* There are possibility to reduce test time further by using
|
|
* smaller vector sizes for different data types where applicable.
|
|
*/
|
|
constexpr uint32_t vector_byte = 64;
|
|
|
|
template <typename T>
|
|
constexpr uint32_t num_elements_per_vector()
|
|
{
|
|
return vector_byte / sizeof(T);
|
|
}
|
|
|
|
/** Scale data types */
|
|
const auto ScaleDataTypes = framework::dataset::make("DataType",
|
|
{
|
|
DataType::U8,
|
|
DataType::S16,
|
|
DataType::F32,
|
|
});
|
|
|
|
/** Quantization information data set */
|
|
const auto QuantizationInfoSet = framework::dataset::make("QuantizationInfo",
|
|
{
|
|
QuantizationInfo(0.5f, -10),
|
|
});
|
|
|
|
/** Tolerance */
|
|
constexpr AbsoluteTolerance<uint8_t> tolerance_u8(1);
|
|
constexpr AbsoluteTolerance<int16_t> tolerance_s16(1);
|
|
RelativeTolerance<float> tolerance_f32(0.05);
|
|
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
|
|
RelativeTolerance<half> tolerance_f16(half(0.1));
|
|
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
|
|
|
|
constexpr float tolerance_num_s16 = 0.01f;
|
|
constexpr float tolerance_num_f32 = 0.01f;
|
|
} // namespace
|
|
|
|
TEST_SUITE(NEON)
|
|
TEST_SUITE(Scale)
|
|
TEST_SUITE(Validate)
|
|
|
|
/** Validate test suite is to test ARM_COMPUTE_RETURN_ON_* macros
|
|
* we use to check the validity of given arguments in @ref NEScale
|
|
* and subsequent call to @ref NEScaleKernel.
|
|
* Since this is using validate() of @ref NEScale, which pre-adjust
|
|
* arguments for @ref NEScaleKernel, the following conditions in
|
|
* the kernel are not currently tested.
|
|
* - The same input and output
|
|
* - Data type of offset, dx and dy
|
|
* This suite also tests two different validate() APIs - one is
|
|
* using @ref ScaleKernelInfo and the other one is more verbose
|
|
* one calls the other one - in the same test case. Even though
|
|
* there are possibility that it makes debugging for regression
|
|
* harder, belows are reasons of this test case implementation.
|
|
* - The more verbose one is just a wrapper function calls
|
|
* the other one without any additional logic. So we are
|
|
* safe to merge two tests into one.
|
|
* - A large amount of code duplication is test suite can be prevented.
|
|
*/
|
|
|
|
const auto input_shape = TensorShape{ 2, 3, 3, 2 };
|
|
const auto output_shape = TensorShape{ 4, 6, 3, 2 };
|
|
|
|
constexpr auto default_data_type = DataType::U8;
|
|
constexpr auto default_data_layout = DataLayout::NHWC;
|
|
constexpr auto default_interpolation_policy = InterpolationPolicy::NEAREST_NEIGHBOR;
|
|
constexpr auto default_border_mode = BorderMode::CONSTANT;
|
|
constexpr auto default_sampling_policy = SamplingPolicy::CENTER;
|
|
|
|
TEST_CASE(NullPtr, framework::DatasetMode::ALL)
|
|
{
|
|
const auto input = TensorInfo{ input_shape, 1, default_data_type, default_data_layout };
|
|
const auto output = TensorInfo{ output_shape, 1, default_data_type, default_data_layout };
|
|
Status result{};
|
|
|
|
// nullptr is given as input
|
|
result = NEScale::validate(nullptr, &output, ScaleKernelInfo{ default_interpolation_policy, default_border_mode, PixelValue(), SamplingPolicy::CENTER, false });
|
|
ARM_COMPUTE_EXPECT(bool(result) == false, framework::LogLevel::ERRORS);
|
|
|
|
// nullptr is given as output
|
|
result = NEScale::validate(&input, nullptr, ScaleKernelInfo{ default_interpolation_policy, default_border_mode, PixelValue(), SamplingPolicy::CENTER, false });
|
|
ARM_COMPUTE_EXPECT(bool(result) == false, framework::LogLevel::ERRORS);
|
|
}
|
|
|
|
TEST_CASE(SupportDataType, framework::DatasetMode::ALL)
|
|
{
|
|
const std::map<DataType, bool> supported_data_types =
|
|
{
|
|
{ DataType::U8, true },
|
|
{ DataType::S8, false },
|
|
{ DataType::QSYMM8, false },
|
|
{ DataType::QASYMM8, true },
|
|
{ DataType::QASYMM8_SIGNED, true },
|
|
{ DataType::QSYMM8_PER_CHANNEL, false },
|
|
{ DataType::U16, false },
|
|
{ DataType::S16, true },
|
|
{ DataType::QSYMM16, false },
|
|
{ DataType::QASYMM16, false },
|
|
{ DataType::U32, false },
|
|
{ DataType::S32, false },
|
|
{ DataType::U64, false },
|
|
{ DataType::S64, false },
|
|
{ DataType::BFLOAT16, false },
|
|
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
|
|
{ DataType::F16, true },
|
|
#else // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
|
|
{ DataType::F16, false },
|
|
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
|
|
{ DataType::F32, true },
|
|
{ DataType::F64, false },
|
|
{ DataType::SIZET, false },
|
|
};
|
|
Status result{};
|
|
for(auto &kv : supported_data_types)
|
|
{
|
|
const auto input = TensorInfo{ input_shape, 1, kv.first, default_data_layout };
|
|
const auto output = TensorInfo{ output_shape, 1, kv.first, default_data_layout };
|
|
|
|
result = NEScale::validate(&input, &output, ScaleKernelInfo{ default_interpolation_policy, default_border_mode, PixelValue(), SamplingPolicy::CENTER, false });
|
|
ARM_COMPUTE_EXPECT(bool(result) == kv.second, framework::LogLevel::ERRORS);
|
|
}
|
|
}
|
|
|
|
TEST_CASE(MissmatchingDataType, framework::DatasetMode::ALL)
|
|
{
|
|
constexpr auto non_default_data_type = DataType::F32;
|
|
|
|
const auto input = TensorInfo{ input_shape, 1, default_data_type, default_data_layout };
|
|
const auto output = TensorInfo{ output_shape, 1, non_default_data_type, default_data_layout };
|
|
Status result{};
|
|
|
|
result = NEScale::validate(&input, &output, ScaleKernelInfo{ default_interpolation_policy, default_border_mode, PixelValue(), SamplingPolicy::CENTER, false });
|
|
ARM_COMPUTE_EXPECT(bool(result) == false, framework::LogLevel::ERRORS);
|
|
}
|
|
|
|
TEST_CASE(UsePadding, framework::DatasetMode::ALL)
|
|
{
|
|
const auto input = TensorInfo{ input_shape, 1, default_data_type, default_data_layout };
|
|
const auto output = TensorInfo{ output_shape, 1, default_data_type, default_data_layout };
|
|
Status result{};
|
|
|
|
// Padding is not supported anymore
|
|
constexpr auto border_mode = BorderMode::CONSTANT;
|
|
constexpr bool use_padding = true;
|
|
|
|
result = NEScale::validate(&input, &output, ScaleKernelInfo{ default_interpolation_policy, border_mode, PixelValue(), default_sampling_policy, use_padding });
|
|
ARM_COMPUTE_EXPECT(bool(result) == false, framework::LogLevel::ERRORS);
|
|
}
|
|
|
|
TEST_CASE(AreaWithNHWC, framework::DatasetMode::ALL)
|
|
{
|
|
// InterpolationPolicy::AREA is not supported for NHWC
|
|
constexpr auto interpolation_policy = InterpolationPolicy::AREA;
|
|
constexpr auto data_layout = DataLayout::NHWC;
|
|
|
|
const auto input = TensorInfo{ input_shape, 1, default_data_type, data_layout };
|
|
const auto output = TensorInfo{ output_shape, 1, default_data_type, data_layout };
|
|
Status result{};
|
|
|
|
result = NEScale::validate(&input, &output, ScaleKernelInfo{ interpolation_policy, default_border_mode, PixelValue(), SamplingPolicy::CENTER, false });
|
|
ARM_COMPUTE_EXPECT(bool(result) == false, framework::LogLevel::ERRORS);
|
|
}
|
|
|
|
TEST_CASE(AreaWithNonU8, framework::DatasetMode::ALL)
|
|
{
|
|
// InterpolationPolicy::AREA only supports U8
|
|
constexpr auto interpolation_policy = InterpolationPolicy::AREA;
|
|
constexpr auto data_type = DataType::F32;
|
|
constexpr auto data_layout = DataLayout::NCHW;
|
|
|
|
const auto input = TensorInfo{ input_shape, 1, data_type, data_layout };
|
|
const auto output = TensorInfo{ output_shape, 1, data_type, data_layout };
|
|
Status result{};
|
|
|
|
result = NEScale::validate(&input, &output, ScaleKernelInfo{ interpolation_policy, default_border_mode, PixelValue(), SamplingPolicy::CENTER, false });
|
|
ARM_COMPUTE_EXPECT(bool(result) == false, framework::LogLevel::ERRORS);
|
|
}
|
|
|
|
TEST_CASE(AlignedCornerNotSupported, framework::DatasetMode::ALL)
|
|
{
|
|
// Aligned corners require sampling policy to be TOP_LEFT.
|
|
constexpr auto interpolation_policy = InterpolationPolicy::BILINEAR;
|
|
constexpr bool align_corners = true;
|
|
constexpr auto sampling_policy = SamplingPolicy::CENTER;
|
|
|
|
const auto input = TensorInfo{ input_shape, 1, default_data_type, default_data_layout };
|
|
const auto output = TensorInfo{ output_shape, 1, default_data_type, default_data_layout };
|
|
Status result{};
|
|
|
|
result = NEScale::validate(&input, &output, ScaleKernelInfo{ interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false, align_corners });
|
|
ARM_COMPUTE_EXPECT(bool(result) == false, framework::LogLevel::ERRORS);
|
|
}
|
|
TEST_SUITE_END() // Validate
|
|
|
|
DATA_TEST_CASE(CheckNoPadding, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::Medium4DShapes(),
|
|
framework::dataset::make("DataType", { DataType::F32, DataType::QASYMM8 })),
|
|
framework::dataset::make("InterpolationPolicy", { InterpolationPolicy::BILINEAR, InterpolationPolicy::NEAREST_NEIGHBOR })),
|
|
framework::dataset::make("SamplingPolicy", { SamplingPolicy::CENTER, SamplingPolicy::TOP_LEFT })),
|
|
framework::dataset::make("DataLayout", { DataLayout::NHWC, DataLayout::NCHW })),
|
|
shape, data_type, interpolation_policy, sampling_policy, data_layout)
|
|
{
|
|
constexpr auto default_border_mode = BorderMode::CONSTANT;
|
|
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false);
|
|
|
|
// Create tensors
|
|
Tensor src = create_tensor<Tensor>(shape, data_type);
|
|
src.info()->set_data_layout(data_layout);
|
|
|
|
const float scale_x = 0.5f;
|
|
const float scale_y = 0.5f;
|
|
TensorShape shape_scaled(shape);
|
|
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
|
|
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
|
|
shape_scaled.set(idx_width, shape[idx_width] * scale_x, /* apply_dim_correction = */ false);
|
|
shape_scaled.set(idx_height, shape[idx_height] * scale_y, /* apply_dim_correction = */ false);
|
|
Tensor dst = create_tensor<Tensor>(shape_scaled, data_type);
|
|
|
|
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
|
|
// Create and configure function
|
|
NEScale scale;
|
|
scale.configure(&src, &dst, info);
|
|
|
|
validate(src.info()->padding(), PaddingSize(0, 0, 0, 0));
|
|
validate(dst.info()->padding(), PaddingSize(0, 0, 0, 0));
|
|
}
|
|
|
|
DATA_TEST_CASE(CheckNoPaddingInterpAREA, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::Medium4DShapes(),
|
|
framework::dataset::make("DataType", { DataType::U8 })),
|
|
framework::dataset::make("InterpolationPolicy", { InterpolationPolicy::AREA })),
|
|
framework::dataset::make("SamplingPolicy", { SamplingPolicy::CENTER, SamplingPolicy::TOP_LEFT })),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
|
|
shape, data_type, interpolation_policy, sampling_policy, data_layout)
|
|
{
|
|
constexpr auto default_border_mode = BorderMode::CONSTANT;
|
|
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false);
|
|
|
|
// Create tensors
|
|
Tensor src = create_tensor<Tensor>(shape, data_type);
|
|
src.info()->set_data_layout(data_layout);
|
|
|
|
const float scale_x = 0.5f;
|
|
const float scale_y = 0.5f;
|
|
TensorShape shape_scaled(shape);
|
|
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
|
|
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
|
|
shape_scaled.set(idx_width, shape[idx_width] * scale_x, /* apply_dim_correction = */ false);
|
|
shape_scaled.set(idx_height, shape[idx_height] * scale_y, /* apply_dim_correction = */ false);
|
|
|
|
Tensor dst = create_tensor<Tensor>(shape, data_type);
|
|
|
|
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
|
|
// Create and configure function
|
|
NEScale scale;
|
|
scale.configure(&src, &dst, info);
|
|
|
|
validate(src.info()->padding(), PaddingSize(0, 0, 0, 0));
|
|
validate(dst.info()->padding(), PaddingSize(0, 0, 0, 0));
|
|
}
|
|
|
|
template <typename T>
|
|
using NEScaleFixture = ScaleValidationFixture<Tensor, Accessor, NEScale, T>;
|
|
template <typename T>
|
|
using NEScaleQuantizedFixture = ScaleValidationQuantizedFixture<Tensor, Accessor, NEScale, T>;
|
|
|
|
TEST_SUITE(Float)
|
|
TEST_SUITE(FP32)
|
|
const auto f32_shape = combine((SCALE_SHAPE_DATASET(num_elements_per_vector<float>())), framework::dataset::make("DataType", DataType::F32));
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEScaleFixture<float>, framework::DatasetMode::ALL, ASSEMBLE_DATASET(f32_shape, ScaleSamplingPolicySet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_f32, tolerance_num_f32);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmallAlignCorners, NEScaleFixture<float>, framework::DatasetMode::ALL, ASSEMBLE_DATASET(f32_shape, ScaleAlignCornersSamplingPolicySet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_f32, tolerance_num_f32);
|
|
}
|
|
TEST_SUITE_END() // FP32
|
|
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
|
|
TEST_SUITE(FP16)
|
|
const auto f16_shape = combine((SCALE_SHAPE_DATASET(num_elements_per_vector<half>())), framework::dataset::make("DataType", DataType::F16));
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEScaleFixture<half>, framework::DatasetMode::ALL, ASSEMBLE_DATASET(f16_shape, ScaleSamplingPolicySet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_f16);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmallAlignCorners, NEScaleFixture<half>, framework::DatasetMode::ALL, ASSEMBLE_DATASET(f16_shape, ScaleAlignCornersSamplingPolicySet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_f16);
|
|
}
|
|
TEST_SUITE_END() // FP16
|
|
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
|
|
TEST_SUITE_END() // Float
|
|
|
|
TEST_SUITE(Integer)
|
|
TEST_SUITE(U8)
|
|
const auto u8_shape = combine((SCALE_SHAPE_DATASET(num_elements_per_vector<uint8_t>())), framework::dataset::make("DataType", DataType::U8));
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEScaleFixture<uint8_t>, framework::DatasetMode::ALL, ASSEMBLE_DATASET(u8_shape, ScaleSamplingPolicySet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_u8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmallAlignCorners, NEScaleFixture<uint8_t>, framework::DatasetMode::ALL, ASSEMBLE_DATASET(u8_shape, ScaleAlignCornersSamplingPolicySet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_u8);
|
|
}
|
|
TEST_SUITE_END() // U8
|
|
TEST_SUITE(S16)
|
|
const auto s16_shape = combine((SCALE_SHAPE_DATASET(num_elements_per_vector<int16_t>())), framework::dataset::make("DataType", DataType::S16));
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEScaleFixture<int16_t>, framework::DatasetMode::ALL, ASSEMBLE_DATASET(s16_shape, ScaleSamplingPolicySet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_s16, tolerance_num_s16);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmallAlignCorners, NEScaleFixture<int16_t>, framework::DatasetMode::ALL, ASSEMBLE_DATASET(s16_shape, ScaleAlignCornersSamplingPolicySet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_s16, tolerance_num_s16);
|
|
}
|
|
TEST_SUITE_END() // S16
|
|
TEST_SUITE_END() // Integer
|
|
|
|
TEST_SUITE(Quantized)
|
|
TEST_SUITE(QASYMM8)
|
|
const auto qasymm8_shape = combine((SCALE_SHAPE_DATASET(num_elements_per_vector<uint8_t>())), framework::dataset::make("DataType", DataType::QASYMM8));
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEScaleQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, ASSEMBLE_QUANTIZED_DATASET(qasymm8_shape, ScaleSamplingPolicySet, QuantizationInfoSet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_u8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmallAlignCorners, NEScaleQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, ASSEMBLE_QUANTIZED_DATASET(qasymm8_shape, ScaleAlignCornersSamplingPolicySet,
|
|
QuantizationInfoSet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_u8);
|
|
}
|
|
TEST_SUITE_END() // QASYMM8
|
|
TEST_SUITE(QASYMM8_SIGNED)
|
|
const auto qasymm8_signed_shape = combine((SCALE_SHAPE_DATASET(num_elements_per_vector<int8_t>())), framework::dataset::make("DataType", DataType::QASYMM8_SIGNED));
|
|
constexpr AbsoluteTolerance<int8_t> tolerance_qasymm8_signed{ 1 };
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEScaleQuantizedFixture<int8_t>, framework::DatasetMode::ALL, ASSEMBLE_QUANTIZED_DATASET(qasymm8_signed_shape, ScaleSamplingPolicySet, QuantizationInfoSet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_qasymm8_signed);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmallAlignCorners, NEScaleQuantizedFixture<int8_t>, framework::DatasetMode::ALL, ASSEMBLE_QUANTIZED_DATASET(qasymm8_signed_shape, ScaleAlignCornersSamplingPolicySet,
|
|
QuantizationInfoSet))
|
|
{
|
|
//Create valid region
|
|
TensorInfo src_info(_shape, 1, _data_type);
|
|
ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _policy, _sampling_policy, (_border_mode == BorderMode::UNDEFINED));
|
|
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, valid_region, tolerance_qasymm8_signed);
|
|
}
|
|
TEST_SUITE_END() // QASYMM8_SIGNED
|
|
TEST_SUITE_END() // Quantized
|
|
|
|
TEST_SUITE_END() // Scale
|
|
TEST_SUITE_END() // NEON
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|