166 lines
6.3 KiB
C++
166 lines
6.3 KiB
C++
/*
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* Copyright (c) 2019-2020 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/runtime/RuntimeContext.h"
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#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
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#include "arm_compute/runtime/SchedulerFactory.h"
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#include "arm_compute/runtime/Tensor.h"
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#include "tests/Globals.h"
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#include "tests/NEON/Accessor.h"
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#include "tests/Utils.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/validation/Validation.h"
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#include "tests/validation/reference/ActivationLayer.h"
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#include <memory>
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#include <random>
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#if !defined(BARE_METAL)
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#include <thread>
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#endif // !defined(BARE_METAL)
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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(NEON)
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TEST_SUITE(UNIT)
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TEST_SUITE(RuntimeContext)
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TEST_CASE(Scheduler, framework::DatasetMode::ALL)
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{
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using namespace arm_compute;
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// Create a runtime context object
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RuntimeContext ctx;
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// Check if it's been initialised properly
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ARM_COMPUTE_EXPECT(ctx.scheduler() != nullptr, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(ctx.asset_manager() == nullptr, framework::LogLevel::ERRORS);
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// Create a Scheduler
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auto scheduler = SchedulerFactory::create();
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ctx.set_scheduler(scheduler.get());
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// Check if the scheduler has been properly setup
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ARM_COMPUTE_EXPECT(ctx.scheduler() != nullptr, framework::LogLevel::ERRORS);
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// Create a new activation function
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NEActivationLayer act_layer(&ctx);
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Tensor src = create_tensor<Tensor>(TensorShape(32, 32), DataType::F32, 1);
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Tensor dst = create_tensor<Tensor>(TensorShape(32, 32), DataType::F32, 1);
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act_layer.configure(&src, &dst, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR));
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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float min_bound = 0;
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float max_bound = 0;
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std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<float>(ActivationLayerInfo::ActivationFunction::LINEAR, DataType::F32);
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std::uniform_real_distribution<> distribution(min_bound, max_bound);
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library->fill(Accessor(src), distribution, 0);
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// Compute function
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act_layer.run();
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}
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#if !defined(BARE_METAL)
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// This test tries scheduling work concurrently from two independent threads
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TEST_CASE(MultipleThreadedScheduller, framework::DatasetMode::ALL)
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{
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// Create a runtime context object for thread 1
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RuntimeContext ctx1;
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// Create a runtime context object for thread 2
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RuntimeContext ctx2;
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// Create a new activation function
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NEActivationLayer act_layer_thread0(&ctx1);
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NEActivationLayer act_layer_thread1(&ctx2);
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const TensorShape tensor_shape(128, 128);
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Tensor src_t0 = create_tensor<Tensor>(tensor_shape, DataType::F32, 1);
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Tensor dst_t0 = create_tensor<Tensor>(tensor_shape, DataType::F32, 1);
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Tensor src_t1 = create_tensor<Tensor>(tensor_shape, DataType::F32, 1);
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Tensor dst_t1 = create_tensor<Tensor>(tensor_shape, DataType::F32, 1);
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ActivationLayerInfo activation_info(ActivationLayerInfo::ActivationFunction::LINEAR);
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act_layer_thread0.configure(&src_t0, &dst_t0, activation_info);
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act_layer_thread1.configure(&src_t1, &dst_t1, activation_info);
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ARM_COMPUTE_EXPECT(src_t0.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst_t0.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(src_t1.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst_t1.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src_t0.allocator()->allocate();
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dst_t0.allocator()->allocate();
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src_t1.allocator()->allocate();
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dst_t1.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src_t0.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!src_t1.info()->is_resizable(), framework::LogLevel::ERRORS);
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float min_bound = 0;
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float max_bound = 0;
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std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<float>(ActivationLayerInfo::ActivationFunction::LINEAR, DataType::F32);
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std::uniform_real_distribution<> distribution(min_bound, max_bound);
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library->fill(Accessor(src_t0), distribution, 0);
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library->fill(Accessor(src_t1), distribution, 0);
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std::thread neon_thread1([&] { act_layer_thread0.run(); });
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std::thread neon_thread2([&] { act_layer_thread1.run(); });
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neon_thread1.join();
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neon_thread2.join();
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Window window;
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window.use_tensor_dimensions(dst_t0.info()->tensor_shape());
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Iterator t0_it(&dst_t0, window);
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Iterator t1_it(&dst_t1, window);
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execute_window_loop(window, [&](const Coordinates &)
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{
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const bool match = (*reinterpret_cast<float *>(t0_it.ptr()) == *reinterpret_cast<float *>(t1_it.ptr()));
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ARM_COMPUTE_EXPECT(match, framework::LogLevel::ERRORS);
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},
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t0_it, t1_it);
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}
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#endif // !defined(BARE_METAL)
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TEST_SUITE_END() // RuntimeContext
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TEST_SUITE_END() // UNIT
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TEST_SUITE_END() // NEON
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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