/* * Copyright (c) 2017-2021 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 info_clone = info.clone(); ARM_COMPUTE_ASSERT(info_clone != nullptr); 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 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); } /** Validates lock paddings flag*/ TEST_CASE(SubTensorPaddingExpansion, framework::DatasetMode::ALL) { TensorInfo tensor_info(TensorShape(23U, 17U, 3U), 1, DataType::F32); tensor_info.set_lock_paddings(true); // Now lock padding is set to true, therefore the extend padding would fail ARM_COMPUTE_EXPECT_THROW(tensor_info.extend_padding(PaddingSize(2, 1)), framework::LogLevel::ERRORS); } TEST_SUITE_END() // TensorInfo TEST_SUITE_END() // UNIT } // namespace validation } // namespace test } // namespace arm_compute