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Diffstat (limited to 'tests/validation/NEON/UNIT/RuntimeContext.cpp')
| -rw-r--r-- | tests/validation/NEON/UNIT/RuntimeContext.cpp | 166 |
1 files changed, 166 insertions, 0 deletions
diff --git a/tests/validation/NEON/UNIT/RuntimeContext.cpp b/tests/validation/NEON/UNIT/RuntimeContext.cpp new file mode 100644 index 0000000000..05670a94e8 --- /dev/null +++ b/tests/validation/NEON/UNIT/RuntimeContext.cpp @@ -0,0 +1,166 @@ +/* + * Copyright (c) 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/RuntimeContext.h" + +#include "arm_compute/runtime/CPP/CPPScheduler.h" +#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "support/ToolchainSupport.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 CPPScheduler + CPPScheduler scheduler; + ctx.set_scheduler(&scheduler); + // 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 |