arm_compute v19.08
diff --git a/examples/cl_cache.cpp b/examples/cl_cache.cpp
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+/*
+ * 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/CL/CLFunctions.h"
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/CLHelpers.h"
+#include "arm_compute/runtime/CL/CLScheduler.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);
+}