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/*
* 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/functions/CLPermute.h"
#include "arm_compute/runtime/CL/Utils.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);
}
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