192 lines
8.3 KiB
C++
192 lines
8.3 KiB
C++
/*
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* Copyright (c) 2017-2019 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/core/TensorInfo.h"
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#include "arm_compute/core/Types.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Validation.h"
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#include "utils/TypePrinter.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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TEST_SUITE(UNIT)
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TEST_SUITE(TensorInfo)
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// *INDENT-OFF*
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// clang-format off
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/** Validates TensorInfo Autopadding */
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DATA_TEST_CASE(AutoPadding, framework::DatasetMode::ALL, zip(zip(zip(
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framework::dataset::make("TensorShape", {
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TensorShape{},
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TensorShape{ 10U },
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TensorShape{ 10U, 10U },
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TensorShape{ 10U, 10U, 10U },
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TensorShape{ 10U, 10U, 10U, 10U },
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TensorShape{ 10U, 10U, 10U, 10U, 10U },
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TensorShape{ 10U, 10U, 10U, 10U, 10U, 10U }}),
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framework::dataset::make("PaddingSize", {
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PaddingSize{ 0, 0, 0, 0 },
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PaddingSize{ 0, 36, 0, 4 },
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PaddingSize{ 4, 36, 4, 4 },
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PaddingSize{ 4, 36, 4, 4 },
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PaddingSize{ 4, 36, 4, 4 },
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PaddingSize{ 4, 36, 4, 4 },
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PaddingSize{ 4, 36, 4, 4 }})),
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framework::dataset::make("Strides", {
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Strides{},
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Strides{ 1U, 50U },
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Strides{ 1U, 50U },
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Strides{ 1U, 50U, 900U },
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Strides{ 1U, 50U, 900U, 9000U },
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Strides{ 1U, 50U, 900U, 9000U, 90000U },
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Strides{ 1U, 50U, 900U, 9000U, 90000U, 900000U }})),
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framework::dataset::make("Offset", { 0U, 4U, 204U, 204U, 204U, 204U, 204U })),
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shape, auto_padding, strides, offset)
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{
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TensorInfo info{ shape, Format::U8 };
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ARM_COMPUTE_EXPECT(!info.has_padding(), framework::LogLevel::ERRORS);
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info.auto_padding();
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validate(info.padding(), auto_padding);
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ARM_COMPUTE_EXPECT(compare_dimensions(info.strides_in_bytes(), strides), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.offset_first_element_in_bytes() == offset, framework::LogLevel::ERRORS);
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}
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// clang-format on
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// *INDENT-ON*
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/** Validates that TensorInfo is clonable */
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TEST_CASE(Clone, framework::DatasetMode::ALL)
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{
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// Create tensor info
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TensorInfo info(TensorShape(23U, 17U, 3U), // tensor shape
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1, // number of channels
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DataType::F32); // data type
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// Get clone of current tensor info
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std::unique_ptr<ITensorInfo> info_clone = info.clone();
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ARM_COMPUTE_EXPECT(info_clone != nullptr, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info_clone->total_size() == info.total_size(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info_clone->num_channels() == info.num_channels(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info_clone->data_type() == info.data_type(), framework::LogLevel::ERRORS);
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}
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/** Validates that TensorInfo can chain multiple set commands */
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TEST_CASE(TensorInfoBuild, framework::DatasetMode::ALL)
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{
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// Create tensor info
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TensorInfo info(TensorShape(23U, 17U, 3U), // tensor shape
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1, // number of channels
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DataType::F32); // data type
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// Update data type and number of channels
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info.set_data_type(DataType::S32).set_num_channels(3);
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ARM_COMPUTE_EXPECT(info.data_type() == DataType::S32, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.num_channels() == 3, framework::LogLevel::ERRORS);
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// Update data type and set quantization info
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info.set_data_type(DataType::QASYMM8).set_quantization_info(QuantizationInfo(0.5f, 15));
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ARM_COMPUTE_EXPECT(info.data_type() == DataType::QASYMM8, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.quantization_info() == QuantizationInfo(0.5f, 15), framework::LogLevel::ERRORS);
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// Update tensor shape
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info.set_tensor_shape(TensorShape(13U, 15U));
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ARM_COMPUTE_EXPECT(info.tensor_shape() == TensorShape(13U, 15U), framework::LogLevel::ERRORS);
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}
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/** Validates empty quantization info */
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TEST_CASE(NoQuantizationInfo, framework::DatasetMode::ALL)
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{
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// Create tensor info
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const TensorInfo info(TensorShape(32U, 16U), 1, DataType::F32);
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// Check quantization information
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ARM_COMPUTE_EXPECT(info.quantization_info().empty(), framework::LogLevel::ERRORS);
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}
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/** Validates symmetric quantization info */
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TEST_CASE(SymmQuantizationInfo, framework::DatasetMode::ALL)
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{
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// Create tensor info
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const float scale = 0.25f;
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const TensorInfo info(TensorShape(32U, 16U), 1, DataType::QSYMM8, QuantizationInfo(scale));
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// Check quantization information
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ARM_COMPUTE_EXPECT(!info.quantization_info().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!info.quantization_info().scale().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.quantization_info().scale().size() == 1, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.quantization_info().offset().empty(), framework::LogLevel::ERRORS);
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UniformQuantizationInfo qinfo = info.quantization_info().uniform();
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ARM_COMPUTE_EXPECT(qinfo.scale == scale, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(qinfo.offset == 0.f, framework::LogLevel::ERRORS);
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}
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/** Validates asymmetric quantization info */
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TEST_CASE(AsymmQuantizationInfo, framework::DatasetMode::ALL)
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{
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// Create tensor info
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const float scale = 0.25f;
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const int32_t offset = 126;
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const TensorInfo info(TensorShape(32U, 16U), 1, DataType::QSYMM8, QuantizationInfo(scale, offset));
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// Check quantization information
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ARM_COMPUTE_EXPECT(!info.quantization_info().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!info.quantization_info().scale().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.quantization_info().scale().size() == 1, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!info.quantization_info().offset().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.quantization_info().offset().size() == 1, framework::LogLevel::ERRORS);
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UniformQuantizationInfo qinfo = info.quantization_info().uniform();
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ARM_COMPUTE_EXPECT(qinfo.scale == scale, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(qinfo.offset == offset, framework::LogLevel::ERRORS);
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}
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/** Validates symmetric per channel quantization info */
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TEST_CASE(SymmPerChannelQuantizationInfo, framework::DatasetMode::ALL)
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{
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// Create tensor info
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const std::vector<float> scale = { 0.25f, 1.4f, 3.2f, 2.3f, 4.7f };
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const TensorInfo info(TensorShape(32U, 16U), 1, DataType::QSYMM8_PER_CHANNEL, QuantizationInfo(scale));
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// Check quantization information
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ARM_COMPUTE_EXPECT(!info.quantization_info().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!info.quantization_info().scale().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.quantization_info().scale().size() == scale.size(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(info.quantization_info().offset().empty(), framework::LogLevel::ERRORS);
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}
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TEST_SUITE_END() // TensorInfoValidation
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TEST_SUITE_END()
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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