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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/runtime/TensorAllocator.h"
#include "arm_compute/core/utils/misc/Utility.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/MemoryRegion.h"
#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
#include "support/ToolchainSupport.h"
#include "tests/Globals.h"
#include "tests/Utils.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/validation/Validation.h"
#include "tests/validation/reference/ActivationLayer.h"
#include <memory>
#include <random>
namespace arm_compute
{
namespace test
{
namespace validation
{
TEST_SUITE(NEON)
TEST_SUITE(UNIT)
TEST_SUITE(TensorAllocator)
TEST_CASE(ImportMemory, framework::DatasetMode::ALL)
{
// Init tensor info
TensorInfo info(TensorShape(24U, 16U, 3U), 1, DataType::F32);
// Allocate memory buffer
const size_t total_size = info.total_size();
auto data = support::cpp14::make_unique<uint8_t[]>(total_size);
// Negative case : Import nullptr
Tensor t1;
t1.allocator()->init(info);
ARM_COMPUTE_EXPECT(!bool(t1.allocator()->import_memory(nullptr)), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(t1.info()->is_resizable(), framework::LogLevel::ERRORS);
// Negative case : Import misaligned pointer
Tensor t2;
const size_t required_alignment = 339;
t2.allocator()->init(info, required_alignment);
ARM_COMPUTE_EXPECT(!bool(t2.allocator()->import_memory(data.get())), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(t2.info()->is_resizable(), framework::LogLevel::ERRORS);
// Negative case : Import memory to a tensor that is memory managed
Tensor t3;
MemoryGroup mg;
t3.allocator()->set_associated_memory_group(&mg);
ARM_COMPUTE_EXPECT(!bool(t3.allocator()->import_memory(data.get())), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(t3.info()->is_resizable(), framework::LogLevel::ERRORS);
// Positive case : Set raw pointer
Tensor t4;
t4.allocator()->init(info);
ARM_COMPUTE_EXPECT(bool(t4.allocator()->import_memory(data.get())), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!t4.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(t4.buffer() == reinterpret_cast<uint8_t *>(data.get()), framework::LogLevel::ERRORS);
t4.allocator()->free();
ARM_COMPUTE_EXPECT(t4.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(t4.buffer() == nullptr, framework::LogLevel::ERRORS);
}
TEST_CASE(ImportMemoryMalloc, framework::DatasetMode::ALL)
{
const ActivationLayerInfo act_info(ActivationLayerInfo::ActivationFunction::RELU);
const TensorShape shape = TensorShape(24U, 16U, 3U);
const DataType data_type = DataType::F32;
// Create tensor
const TensorInfo info(shape, 1, data_type);
const size_t required_alignment = 64;
Tensor tensor;
tensor.allocator()->init(info, required_alignment);
// Create and configure activation function
NEActivationLayer act_func;
act_func.configure(&tensor, nullptr, act_info);
// Allocate and import tensor
const size_t total_size_in_elems = tensor.info()->tensor_shape().total_size();
const size_t total_size_in_bytes = tensor.info()->total_size();
size_t space = total_size_in_bytes + required_alignment;
auto raw_data = support::cpp14::make_unique<uint8_t[]>(space);
void *aligned_ptr = raw_data.get();
support::cpp11::align(required_alignment, total_size_in_bytes, aligned_ptr, space);
ARM_COMPUTE_EXPECT(bool(tensor.allocator()->import_memory(aligned_ptr)), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensor
std::uniform_real_distribution<float> distribution(-5.f, 5.f);
std::mt19937 gen(library->seed());
auto *typed_ptr = reinterpret_cast<float *>(aligned_ptr);
for(unsigned int i = 0; i < total_size_in_elems; ++i)
{
typed_ptr[i] = distribution(gen);
}
// Execute function and sync
act_func.run();
// Validate result by checking that the input has no negative values
for(unsigned int i = 0; i < total_size_in_elems; ++i)
{
ARM_COMPUTE_EXPECT(typed_ptr[i] >= 0, framework::LogLevel::ERRORS);
}
// Release resources
tensor.allocator()->free();
ARM_COMPUTE_EXPECT(tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
}
TEST_CASE(AlignedAlloc, framework::DatasetMode::ALL)
{
// Init tensor info
TensorInfo info(TensorShape(24U, 16U, 3U), 1, DataType::F32);
const size_t requested_alignment = 1024;
Tensor t;
t.allocator()->init(info, requested_alignment);
t.allocator()->allocate();
ARM_COMPUTE_EXPECT(t.buffer() != nullptr, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(t.allocator()->alignment() == requested_alignment, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(arm_compute::utility::check_aligned(reinterpret_cast<void *>(t.buffer()), requested_alignment),
framework::LogLevel::ERRORS);
}
TEST_SUITE_END()
TEST_SUITE_END()
TEST_SUITE_END()
} // namespace validation
} // namespace test
} // namespace arm_compute
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