diff options
Diffstat (limited to 'contrib/convacl')
| -rw-r--r-- | contrib/convacl/CMakeLists.txt | 20 | ||||
| -rw-r--r-- | contrib/convacl/src/io_accessor.cc | 110 | ||||
| -rw-r--r-- | contrib/convacl/src/io_accessor.h | 93 | ||||
| -rw-r--r-- | contrib/convacl/src/nnapi_acl_conv.cc | 239 |
4 files changed, 0 insertions, 462 deletions
diff --git a/contrib/convacl/CMakeLists.txt b/contrib/convacl/CMakeLists.txt deleted file mode 100644 index ca6411211..000000000 --- a/contrib/convacl/CMakeLists.txt +++ /dev/null @@ -1,20 +0,0 @@ -if(NOT BUILD_LABS) - return() -endif(NOT BUILD_LABS) - -if(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - return() -endif(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - -nnfw_find_package(ARMCompute REQUIRED) - -file(GLOB_RECURSE NNAPI_CONVACL_SRCS "src/*.cc") - -link_directories(${CMAKE_INSTALL_PREFIX}/lib) - -add_library(exp_convacl SHARED ${NNAPI_CONVACL_SRCS}) -target_include_directories(exp_convacl PUBLIC ${NNFW_INCLUDE_DIR}) -target_link_libraries(exp_convacl nnfw_util arm_compute_graph) - -# we need the library name to be 'neuralnetworks' and this will do the trick -set_target_properties(exp_convacl PROPERTIES OUTPUT_NAME neuralnetworks) diff --git a/contrib/convacl/src/io_accessor.cc b/contrib/convacl/src/io_accessor.cc deleted file mode 100644 index b7fdee721..000000000 --- a/contrib/convacl/src/io_accessor.cc +++ /dev/null @@ -1,110 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2018 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 "io_accessor.h" -#include <arm_compute/core/Helpers.h> -#include <ostream> - -bool InputAccessor::access_tensor(arm_compute::ITensor &tensor) -{ - // Subtract the mean value from each channel - arm_compute::Window window; - window.use_tensor_dimensions(tensor.info()->tensor_shape()); - - execute_window_loop(window, [&](const arm_compute::Coordinates& id) - { - *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = _test_input; - _test_input += _inc ? 1.0 : 0.0; - - std::cout << "Input " << id.y() << "," << id.x() << " = "; - std::cout << *reinterpret_cast<float *>(tensor.ptr_to_element(id)); - std::cout << std::endl; - }); - return true; -} - -bool OutputAccessor::access_tensor(arm_compute::ITensor &tensor) -{ - // Subtract the mean value from each channel - arm_compute::Window window; - window.use_tensor_dimensions(tensor.info()->tensor_shape()); - - execute_window_loop(window, [&](const arm_compute::Coordinates& id) - { - std::cout << "Output " << id.y() << "," << id.x() << " = "; - std::cout << *reinterpret_cast<float *>(tensor.ptr_to_element(id)); - std::cout << std::endl; - }); - return false; // end the network -} - -bool WeightAccessor::access_tensor(arm_compute::ITensor &tensor) -{ - // Subtract the mean value from each channel - arm_compute::Window window; - window.use_tensor_dimensions(tensor.info()->tensor_shape()); - - execute_window_loop(window, [&](const arm_compute::Coordinates& id) - { - *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = _test_weight; - _test_weight += _inc ? 1.0 : 0.0; - - std::cout << "Weight " << id.y() << "," << id.x() << " = "; - std::cout << *reinterpret_cast<float *>(tensor.ptr_to_element(id)); - std::cout << std::endl; - }); - return true; -} - -bool BiasAccessor::access_tensor(arm_compute::ITensor &tensor) -{ - // Subtract the mean value from each channel - arm_compute::Window window; - window.use_tensor_dimensions(tensor.info()->tensor_shape()); - - execute_window_loop(window, [&](const arm_compute::Coordinates& id) - { - *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = 0.0; - - std::cout << "Bias " << id.y() << "," << id.x() << " = "; - std::cout << *reinterpret_cast<float *>(tensor.ptr_to_element(id)); - std::cout << std::endl; - }); - return true; -} diff --git a/contrib/convacl/src/io_accessor.h b/contrib/convacl/src/io_accessor.h deleted file mode 100644 index 4033020e0..000000000 --- a/contrib/convacl/src/io_accessor.h +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2018 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. - */ -#ifndef __IO_ACCESSOR_H__ -#define __IO_ACCESSOR_H__ - -#include <arm_compute/graph/ITensorAccessor.h> - -class InputAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - InputAccessor(bool inc) : _inc(inc) { _test_input = 1.0; } - InputAccessor(InputAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; - -private: - bool _inc; - float _test_input; -}; - -class OutputAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - OutputAccessor() = default; - OutputAccessor(OutputAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; -}; - -class WeightAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - WeightAccessor(bool inc) : _inc(inc) { _test_weight = 1.0; } - WeightAccessor(WeightAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; - -private: - bool _inc; - float _test_weight; -}; - -class BiasAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - BiasAccessor() = default; - BiasAccessor(BiasAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; -}; - -#endif // __IO_ACCESSOR_H__ diff --git a/contrib/convacl/src/nnapi_acl_conv.cc b/contrib/convacl/src/nnapi_acl_conv.cc deleted file mode 100644 index 091d19497..000000000 --- a/contrib/convacl/src/nnapi_acl_conv.cc +++ /dev/null @@ -1,239 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2018 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 <NeuralNetworks.h> -#include <stdexcept> -#include <iostream> -#include <string> -#include <map> -#include <cassert> -#include <memory> -#include <boost/format.hpp> -// ACL Headers -#include <arm_compute/graph.h> - -#include "util/environment.h" -#include "io_accessor.h" - -// -// Asynchronous Event -// -struct ANeuralNetworksEvent -{ -}; - -int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event) -{ - delete event; -} - -// -// Memory -// -struct ANeuralNetworksMemory -{ - // 1st approach - Store all the data inside ANeuralNetworksMemory object - // 2nd approach - Store metadata only, and defer data loading as much as possible -}; - -int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, ANeuralNetworksMemory** memory) -{ - *memory = new ANeuralNetworksMemory; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory) -{ - delete memory; -} - -// -// Model -// -struct ANeuralNetworksModel -{ - // ANeuralNetworksModel should be a factory for Graph IR (a.k.a ISA Frontend) - // TODO Record # of operands - uint32_t numOperands; - - ANeuralNetworksModel() : numOperands(0) - { - // DO NOTHING - } -}; - -int ANeuralNetworksModel_create(ANeuralNetworksModel** model) -{ - *model = new ANeuralNetworksModel; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksModel_free(ANeuralNetworksModel* model) -{ - delete model; -} - -int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, const ANeuralNetworksOperandType *type) -{ - model->numOperands += 1; - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index, const ANeuralNetworksMemory* memory, size_t offset, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -// -// Compilation -// -struct ANeuralNetworksCompilation -{ - // ANeuralNetworksCompilation should hold a compiled IR -}; - -int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, ANeuralNetworksCompilation** compilation) -{ - *compilation = new ANeuralNetworksCompilation; - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -// -// Execution -// -struct ANeuralNetworksExecution -{ - // ANeuralNetworksExecution corresponds to NPU::Interp::Session - - arm_compute::graph::frontend::Stream graph{0, "ACL_CONV"}; -}; - -int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, ANeuralNetworksExecution** execution) -{ - std::cout << __FUNCTION__ << " +++" << std::endl; - *execution = new ANeuralNetworksExecution; - - using arm_compute::DataType; - using arm_compute::graph::Target; - using arm_compute::graph::TensorDescriptor; - using arm_compute::TensorShape; - using arm_compute::graph::frontend::InputLayer; - using arm_compute::graph::frontend::OutputLayer; - - ANeuralNetworksExecution* execlocal = *execution; - arm_compute::graph::frontend::Stream& graph = execlocal->graph; - - Target target_hint = nnfw::util::get_env_int("NNFW_ACL_USENEON") - ? Target::NEON : Target::CL; - bool autoinc = nnfw::util::get_env_bool("NNFW_TEST_AUTOINC"); - - graph << target_hint - << InputLayer(TensorDescriptor(TensorShape(3U, 3U, 1U, 1U), DataType::F32), - std::unique_ptr<InputAccessor>(new InputAccessor(autoinc))) - << arm_compute::graph::frontend::ConvolutionLayer( - 3U, 3U, 1U, - std::unique_ptr<WeightAccessor>(new WeightAccessor(autoinc)), - std::unique_ptr<BiasAccessor>(new BiasAccessor()), - arm_compute::PadStrideInfo(1, 1, 0, 0)) - << OutputLayer( - std::unique_ptr<OutputAccessor>(new OutputAccessor())); - - std::cout << __FUNCTION__ << " ---" << std::endl; - return ANEURALNETWORKS_NO_ERROR; -} - -// ANeuralNetworksExecution_setInput and ANeuralNetworksExecution_setOutput specify HOST buffer for input/output -int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, ANeuralNetworksEvent** event) -{ - std::cout << __FUNCTION__ << " +++" << std::endl; - *event = new ANeuralNetworksEvent; - - // graph.run() fails with segment fail when only target_hint is added. - // after fix adding 'Tensor' we may call graph.run() - arm_compute::graph::frontend::Stream& graph = execution->graph; - graph.run(); - - std::cout << __FUNCTION__ << " ---" << std::endl; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution) -{ - delete execution; -} |