| /* |
| * Copyright (c) 2019-2020 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/core/CL/OpenCL.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/CL/CLHelpers.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "arm_compute/runtime/CL/Utils.h" |
| #include "arm_compute/runtime/CL/functions/CLPermute.h" |
| #include "utils/Utils.h" |
| |
| using namespace arm_compute; |
| using namespace utils; |
| |
| namespace |
| { |
| } // namespace |
| |
| class CLCacheExample : public Example |
| { |
| public: |
| CLCacheExample() = default; |
| |
| bool do_setup(int argc, char **argv) override |
| { |
| std::cout << "Once the program has run and created the file cache.bin, rerun with --restore_cache." << std::endl; |
| CLScheduler::get().default_init(); |
| |
| if(argc > 1) |
| { |
| std::string argv1 = argv[1]; |
| std::transform(argv1.begin(), argv1.end(), argv1.begin(), ::tolower); |
| if(argv1 == "--restore_cache") |
| { |
| // Load the precompiled kernels from a file into the kernel library, in this way the next time they are needed |
| // compilation won't be required. |
| restore_program_cache_from_file(); |
| } |
| else |
| { |
| std::cout << "Unkown option " << argv1 << std::endl; |
| } |
| } |
| |
| // Initialise shapes |
| init_tensor(TensorShape(8U, 4U, 2U), tensor_nchw, DataType::U8, DataLayout::NCHW); |
| init_tensor(TensorShape(2U, 8U, 4U), tensor_nhwc, DataType::U8, DataLayout::NHWC); |
| init_tensor(TensorShape(8U, 4U, 2U), tensor_nchw_result, DataType::U8, DataLayout::NCHW); |
| |
| // Create the permutation vector to turn a NCHW tensor to NHWC. |
| // The input tensor is NCHW, which means that the fastest changing coordinate is W=8U. |
| // For permutation vectors the fastest changing coordinate is the one on the left too. |
| // Each element in the permutation vector specifies a mapping from the source tensor to the destination one, thus if we |
| // use 2U in the permutation vector's first element we are telling the function to move the channels to the fastest |
| // changing coordinate in the destination tensor. |
| |
| const PermutationVector vector_nchw_to_nhwc(2U, 0U, 1U); |
| permute_nhwc.configure(&tensor_nchw, &tensor_nhwc, vector_nchw_to_nhwc); |
| |
| // Allocate and fill tensors |
| tensor_nhwc.allocator()->allocate(); |
| tensor_nchw.allocator()->allocate(); |
| fill_tensor(tensor_nchw); |
| |
| // Demostrate autoconfigure for the output tensor |
| const PermutationVector vector_nhwc_to_nchw(1U, 2U, 0U); |
| permute_nchw.configure(&tensor_nhwc, &tensor_nchw_result, vector_nhwc_to_nchw); |
| tensor_nchw_result.allocator()->allocate(); |
| |
| // Save the opencl kernels to a file |
| save_program_cache_to_file(); |
| |
| return true; |
| } |
| void do_run() override |
| { |
| permute_nhwc.run(); |
| permute_nchw.run(); |
| } |
| void do_teardown() override |
| { |
| } |
| |
| private: |
| void validate_result(CLTensor &reference, CLTensor &result) |
| { |
| reference.map(); |
| result.map(); |
| Window window; |
| window.use_tensor_dimensions(reference.info()->tensor_shape()); |
| Iterator it_ref(&reference, window); |
| Iterator it_res(&result, window); |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| assert(*reinterpret_cast<unsigned char *>(it_ref.ptr()) == *reinterpret_cast<unsigned char *>(it_res.ptr())); |
| }, |
| it_ref, it_res); |
| reference.unmap(); |
| result.unmap(); |
| } |
| |
| void fill_tensor(CLTensor &tensor) |
| { |
| tensor.map(); |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| Iterator it_tensor(&tensor, window); |
| unsigned char val(0); |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| *reinterpret_cast<unsigned char *>(it_tensor.ptr()) = val++; |
| }, |
| it_tensor); |
| tensor.unmap(); |
| } |
| void init_tensor(const TensorShape shape, CLTensor &tensor, DataType type, DataLayout layout) |
| { |
| tensor.allocator()->init(TensorInfo(shape, 1, type).set_data_layout(layout)); |
| } |
| |
| CLTensor tensor_nchw{}; |
| CLTensor tensor_nhwc{}; |
| CLTensor tensor_nchw_result{}; |
| CLPermute permute_nhwc{}; |
| CLPermute permute_nchw{}; |
| }; |
| |
| /** Main program creating an example that demostrates how to load precompiled kernels from a file. |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments |
| */ |
| int main(int argc, char **argv) |
| { |
| return utils::run_example<CLCacheExample>(argc, argv); |
| } |