arm_compute v17.04
diff --git a/examples/neon_copy_objects.cpp b/examples/neon_copy_objects.cpp
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+/*
+ * Copyright (c) 2016, 2017 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/NEON/NEFunctions.h"
+
+#include "arm_compute/core/Types.h"
+#include "test_helpers/Utils.h"
+
+#include <cstring>
+#include <iostream>
+
+using namespace arm_compute;
+
+void main_neon_copy_objects(int argc, const char **argv)
+{
+ ARM_COMPUTE_UNUSED(argc);
+ ARM_COMPUTE_UNUSED(argv);
+
+ /** [Copy objects example] */
+ constexpr unsigned int width = 4;
+ constexpr unsigned int height = 3;
+ constexpr unsigned int batch = 2;
+
+ auto *src_data = new float[width * height * batch];
+ auto *dst_data = new float[width * height * batch];
+
+ // Fill src_data with dummy values:
+ for(unsigned int b = 0; b < batch; b++)
+ {
+ for(unsigned int h = 0; h < height; h++)
+ {
+ for(unsigned int w = 0; w < width; w++)
+ {
+ src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w);
+ }
+ }
+ }
+
+ Tensor input, output;
+ NESoftmaxLayer softmax;
+
+ // Initialize the tensors dimensions and type:
+ const TensorShape shape(width, height, batch);
+ input.allocator()->init(TensorInfo(shape, 1, DataType::F32));
+ output.allocator()->init(TensorInfo(shape, 1, DataType::F32));
+
+ // Configure softmax:
+ softmax.configure(&input, &output);
+
+ // Allocate the input / output tensors:
+ input.allocator()->allocate();
+ output.allocator()->allocate();
+
+ // Fill the input tensor:
+ // Simplest way: create an iterator to iterate through each element of the input tensor:
+ Window input_window;
+ input_window.use_tensor_dimensions(input.info());
+ std::cout << " Dimensions of the input's iterator:\n";
+ std::cout << " X = [start=" << input_window.x().start() << ", end=" << input_window.x().end() << ", step=" << input_window.x().step() << "]\n";
+ std::cout << " Y = [start=" << input_window.y().start() << ", end=" << input_window.y().end() << ", step=" << input_window.y().step() << "]\n";
+ std::cout << " Z = [start=" << input_window.z().start() << ", end=" << input_window.z().end() << ", step=" << input_window.z().step() << "]\n";
+
+ // Create an iterator:
+ Iterator input_it(&input, input_window);
+
+ // Iterate through the elements of src_data and copy them one by one to the input tensor:
+ // This is equivalent to:
+ // for( unsigned int z = 0; z < batch; ++z)
+ // {
+ // for( unsigned int y = 0; y < height; ++y)
+ // {
+ // for( unsigned int x = 0; x < width; ++x)
+ // {
+ // *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x];
+ // }
+ // }
+ // }
+ // Except it works for an arbitrary number of dimensions
+ execute_window_loop(input_window, [&](const Coordinates & id)
+ {
+ std::cout << "Setting item [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
+ *reinterpret_cast<float *>(input_it.ptr()) = src_data[id.z() * (width * height) + id.y() * width + id.x()];
+ },
+ input_it);
+
+ // Run NEON softmax:
+ softmax.run();
+
+ // More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor:
+ Window output_window;
+ output_window.use_tensor_dimensions(output.info(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element)
+ std::cout << " Dimensions of the output's iterator:\n";
+ std::cout << " X = [start=" << output_window.x().start() << ", end=" << output_window.x().end() << ", step=" << output_window.x().step() << "]\n";
+ std::cout << " Y = [start=" << output_window.y().start() << ", end=" << output_window.y().end() << ", step=" << output_window.y().step() << "]\n";
+ std::cout << " Z = [start=" << output_window.z().start() << ", end=" << output_window.z().end() << ", step=" << output_window.z().step() << "]\n";
+
+ // Create an iterator:
+ Iterator output_it(&output, output_window);
+
+ // Iterate through the rows of the output tensor and copy them to dst_data:
+ // This is equivalent to:
+ // for( unsigned int z = 0; z < batch; ++z)
+ // {
+ // for( unsigned int y = 0; y < height; ++y)
+ // {
+ // memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float));
+ // }
+ // }
+ // Except it works for an arbitrary number of dimensions
+ execute_window_loop(output_window, [&](const Coordinates & id)
+ {
+ std::cout << "Copying one row starting from [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
+ // Copy one whole row:
+ memcpy(dst_data + id.z() * (width * height) + id.y() * width, output_it.ptr(), width * sizeof(float));
+ },
+ output_it);
+
+ delete[] src_data;
+ delete[] dst_data;
+ /** [Copy objects example] */
+}
+
+/** Main program for the copy objects test
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments
+ */
+int main(int argc, const char **argv)
+{
+ return test_helpers::run_example(argc, argv, main_neon_copy_objects);
+}