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/*
* Copyright (c) 2017-2019 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/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/validation/Validation.h"
#include "utils/TypePrinter.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
TEST_SUITE(UNIT)
TEST_SUITE(TensorInfo)
// *INDENT-OFF*
// clang-format off
/** Validates TensorInfo Autopadding */
DATA_TEST_CASE(AutoPadding, framework::DatasetMode::ALL, zip(zip(zip(
framework::dataset::make("TensorShape", {
TensorShape{},
TensorShape{ 10U },
TensorShape{ 10U, 10U },
TensorShape{ 10U, 10U, 10U },
TensorShape{ 10U, 10U, 10U, 10U },
TensorShape{ 10U, 10U, 10U, 10U, 10U },
TensorShape{ 10U, 10U, 10U, 10U, 10U, 10U }}),
framework::dataset::make("PaddingSize", {
PaddingSize{ 0, 0, 0, 0 },
PaddingSize{ 0, 36, 0, 4 },
PaddingSize{ 4, 36, 4, 4 },
PaddingSize{ 4, 36, 4, 4 },
PaddingSize{ 4, 36, 4, 4 },
PaddingSize{ 4, 36, 4, 4 },
PaddingSize{ 4, 36, 4, 4 }})),
framework::dataset::make("Strides", {
Strides{},
Strides{ 1U, 50U },
Strides{ 1U, 50U },
Strides{ 1U, 50U, 900U },
Strides{ 1U, 50U, 900U, 9000U },
Strides{ 1U, 50U, 900U, 9000U, 90000U },
Strides{ 1U, 50U, 900U, 9000U, 90000U, 900000U }})),
framework::dataset::make("Offset", { 0U, 4U, 204U, 204U, 204U, 204U, 204U })),
shape, auto_padding, strides, offset)
{
TensorInfo info{ shape, Format::U8 };
ARM_COMPUTE_EXPECT(!info.has_padding(), framework::LogLevel::ERRORS);
info.auto_padding();
validate(info.padding(), auto_padding);
ARM_COMPUTE_EXPECT(compare_dimensions(info.strides_in_bytes(), strides), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.offset_first_element_in_bytes() == offset, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
/** Validates that TensorInfo is clonable */
TEST_CASE(Clone, framework::DatasetMode::ALL)
{
// Create tensor info
TensorInfo info(TensorShape(23U, 17U, 3U), // tensor shape
1, // number of channels
DataType::F32); // data type
// Get clone of current tensor info
std::unique_ptr<ITensorInfo> info_clone = info.clone();
ARM_COMPUTE_EXPECT(info_clone != nullptr, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info_clone->total_size() == info.total_size(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info_clone->num_channels() == info.num_channels(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info_clone->data_type() == info.data_type(), framework::LogLevel::ERRORS);
}
/** Validates that TensorInfo can chain multiple set commands */
TEST_CASE(TensorInfoBuild, framework::DatasetMode::ALL)
{
// Create tensor info
TensorInfo info(TensorShape(23U, 17U, 3U), // tensor shape
1, // number of channels
DataType::F32); // data type
// Update data type and number of channels
info.set_data_type(DataType::S32).set_num_channels(3);
ARM_COMPUTE_EXPECT(info.data_type() == DataType::S32, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.num_channels() == 3, framework::LogLevel::ERRORS);
// Update data type and set quantization info
info.set_data_type(DataType::QASYMM8).set_quantization_info(QuantizationInfo(0.5f, 15));
ARM_COMPUTE_EXPECT(info.data_type() == DataType::QASYMM8, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.quantization_info() == QuantizationInfo(0.5f, 15), framework::LogLevel::ERRORS);
// Update tensor shape
info.set_tensor_shape(TensorShape(13U, 15U));
ARM_COMPUTE_EXPECT(info.tensor_shape() == TensorShape(13U, 15U), framework::LogLevel::ERRORS);
}
/** Validates empty quantization info */
TEST_CASE(NoQuantizationInfo, framework::DatasetMode::ALL)
{
// Create tensor info
const TensorInfo info(TensorShape(32U, 16U), 1, DataType::F32);
// Check quantization information
ARM_COMPUTE_EXPECT(info.quantization_info().empty(), framework::LogLevel::ERRORS);
}
/** Validates symmetric quantization info */
TEST_CASE(SymmQuantizationInfo, framework::DatasetMode::ALL)
{
// Create tensor info
const float scale = 0.25f;
const TensorInfo info(TensorShape(32U, 16U), 1, DataType::QSYMM8, QuantizationInfo(scale));
// Check quantization information
ARM_COMPUTE_EXPECT(!info.quantization_info().empty(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!info.quantization_info().scale().empty(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.quantization_info().scale().size() == 1, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.quantization_info().offset().empty(), framework::LogLevel::ERRORS);
UniformQuantizationInfo qinfo = info.quantization_info().uniform();
ARM_COMPUTE_EXPECT(qinfo.scale == scale, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(qinfo.offset == 0.f, framework::LogLevel::ERRORS);
}
/** Validates asymmetric quantization info */
TEST_CASE(AsymmQuantizationInfo, framework::DatasetMode::ALL)
{
// Create tensor info
const float scale = 0.25f;
const int32_t offset = 126;
const TensorInfo info(TensorShape(32U, 16U), 1, DataType::QSYMM8, QuantizationInfo(scale, offset));
// Check quantization information
ARM_COMPUTE_EXPECT(!info.quantization_info().empty(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!info.quantization_info().scale().empty(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.quantization_info().scale().size() == 1, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!info.quantization_info().offset().empty(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.quantization_info().offset().size() == 1, framework::LogLevel::ERRORS);
UniformQuantizationInfo qinfo = info.quantization_info().uniform();
ARM_COMPUTE_EXPECT(qinfo.scale == scale, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(qinfo.offset == offset, framework::LogLevel::ERRORS);
}
/** Validates symmetric per channel quantization info */
TEST_CASE(SymmPerChannelQuantizationInfo, framework::DatasetMode::ALL)
{
// Create tensor info
const std::vector<float> scale = { 0.25f, 1.4f, 3.2f, 2.3f, 4.7f };
const TensorInfo info(TensorShape(32U, 16U), 1, DataType::QSYMM8_PER_CHANNEL, QuantizationInfo(scale));
// Check quantization information
ARM_COMPUTE_EXPECT(!info.quantization_info().empty(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!info.quantization_info().scale().empty(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.quantization_info().scale().size() == scale.size(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(info.quantization_info().offset().empty(), framework::LogLevel::ERRORS);
}
TEST_SUITE_END() // TensorInfoValidation
TEST_SUITE_END()
} // namespace validation
} // namespace test
} // namespace arm_compute
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