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/*
* Copyright (c) 2019-2021 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/runtime/RuntimeContext.h"
#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
#include "arm_compute/runtime/SchedulerFactory.h"
#include "arm_compute/runtime/Tensor.h"
#include "tests/Globals.h"
#include "tests/NEON/Accessor.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>
#if !defined(BARE_METAL)
#include <thread>
#endif // !defined(BARE_METAL)
namespace arm_compute
{
namespace test
{
namespace validation
{
TEST_SUITE(NEON)
TEST_SUITE(UNIT)
TEST_SUITE(RuntimeContext)
TEST_CASE(Scheduler, framework::DatasetMode::ALL)
{
using namespace arm_compute;
// Create a runtime context object
RuntimeContext ctx;
// Check if it's been initialised properly
ARM_COMPUTE_EXPECT(ctx.scheduler() != nullptr, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(ctx.asset_manager() == nullptr, framework::LogLevel::ERRORS);
// Create a Scheduler
auto scheduler = SchedulerFactory::create();
ctx.set_scheduler(scheduler.get());
// Check if the scheduler has been properly setup
ARM_COMPUTE_EXPECT(ctx.scheduler() != nullptr, framework::LogLevel::ERRORS);
// Create a new activation function
NEActivationLayer act_layer(&ctx);
Tensor src = create_tensor<Tensor>(TensorShape(32, 32), DataType::F32, 1);
Tensor dst = create_tensor<Tensor>(TensorShape(32, 32), DataType::F32, 1);
act_layer.configure(&src, &dst, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR));
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
float min_bound = 0;
float max_bound = 0;
std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<float>(ActivationLayerInfo::ActivationFunction::LINEAR, DataType::F32);
std::uniform_real_distribution<> distribution(min_bound, max_bound);
library->fill(Accessor(src), distribution, 0);
// Compute function
act_layer.run();
}
#if !defined(BARE_METAL)
// This test tries scheduling work concurrently from two independent threads
TEST_CASE(MultipleThreadedScheduller, framework::DatasetMode::ALL)
{
// Create a runtime context object for thread 1
RuntimeContext ctx1;
// Create a runtime context object for thread 2
RuntimeContext ctx2;
// Create a new activation function
NEActivationLayer act_layer_thread0(&ctx1);
NEActivationLayer act_layer_thread1(&ctx2);
const TensorShape tensor_shape(128, 128);
Tensor src_t0 = create_tensor<Tensor>(tensor_shape, DataType::F32, 1);
Tensor dst_t0 = create_tensor<Tensor>(tensor_shape, DataType::F32, 1);
Tensor src_t1 = create_tensor<Tensor>(tensor_shape, DataType::F32, 1);
Tensor dst_t1 = create_tensor<Tensor>(tensor_shape, DataType::F32, 1);
ActivationLayerInfo activation_info(ActivationLayerInfo::ActivationFunction::LINEAR);
act_layer_thread0.configure(&src_t0, &dst_t0, activation_info);
act_layer_thread1.configure(&src_t1, &dst_t1, activation_info);
ARM_COMPUTE_EXPECT(src_t0.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst_t0.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(src_t1.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst_t1.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src_t0.allocator()->allocate();
dst_t0.allocator()->allocate();
src_t1.allocator()->allocate();
dst_t1.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src_t0.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!src_t1.info()->is_resizable(), framework::LogLevel::ERRORS);
float min_bound = 0;
float max_bound = 0;
std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<float>(ActivationLayerInfo::ActivationFunction::LINEAR, DataType::F32);
std::uniform_real_distribution<> distribution(min_bound, max_bound);
library->fill(Accessor(src_t0), distribution, 0);
library->fill(Accessor(src_t1), distribution, 0);
std::thread neon_thread1([&] { act_layer_thread0.run(); });
std::thread neon_thread2([&] { act_layer_thread1.run(); });
neon_thread1.join();
neon_thread2.join();
Window window;
window.use_tensor_dimensions(dst_t0.info()->tensor_shape());
Iterator t0_it(&dst_t0, window);
Iterator t1_it(&dst_t1, window);
execute_window_loop(window, [&](const Coordinates &)
{
const bool match = (*reinterpret_cast<float *>(t0_it.ptr()) == *reinterpret_cast<float *>(t1_it.ptr()));
ARM_COMPUTE_EXPECT(match, framework::LogLevel::ERRORS);
},
t0_it, t1_it);
}
#endif // !defined(BARE_METAL)
TEST_SUITE_END() // RuntimeContext
TEST_SUITE_END() // UNIT
TEST_SUITE_END() // Neon
} // namespace validation
} // namespace test
} // namespace arm_compute
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