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/*
* Copyright (c) 2017, 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 ARM_COMPUTE_GC
#error "This example needs to be built with -DARM_COMPUTE_GC"
#endif /* ARM_COMPUTE_GC */
#include "arm_compute/runtime/GLES_COMPUTE/GCFunctions.h"
#include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
#include "half/half.hpp"
#include "utils/Utils.h"
using namespace arm_compute;
using namespace utils;
namespace
{
TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &info)
{
TensorShape out_shape(in_shape);
const std::pair<unsigned int, unsigned int> scaled_dims = scaled_dimensions(in_shape.x(),
in_shape.y(),
kernel_shape.x(),
kernel_shape.y(),
info);
out_shape.set(0, scaled_dims.first);
out_shape.set(1, scaled_dims.second);
out_shape.set(2, kernel_shape[3]);
return out_shape;
}
} // namespace
void main_gc_dc(int argc, char **argv)
{
ARM_COMPUTE_UNUSED(argc);
ARM_COMPUTE_UNUSED(argv);
// init instance
GCScheduler::get().default_init();
const TensorShape src_shape = TensorShape{ 11U /* W */, 13U /* H */, 4U /* C */, 3U /* N */ };
const unsigned int kernel_size = 3;
const int stride_x = 1;
const int stride_y = 1;
const int pad_x = 0;
const int pad_y = 0;
const unsigned int num_kernels = 256;
const DataType data_type = DataType::F16;
// generate shape
const TensorShape weights_shape(kernel_size, kernel_size, src_shape.z(), num_kernels);
const TensorShape bias_shape(num_kernels);
const PadStrideInfo pad_info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
// output shape should be 9*11*256*3 (W*H*C*N)
const TensorShape dst_shape = get_output_shape(src_shape, weights_shape, pad_info);
// create tensors
GCTensor src, weights, bias, dst;
src.allocator()->init(TensorInfo(src_shape, 1, data_type));
weights.allocator()->init(TensorInfo(weights_shape, 1, data_type));
bias.allocator()->init(TensorInfo(bias_shape, 1, data_type));
dst.allocator()->init(TensorInfo(dst_shape, 1, data_type));
// configure layer
GCDirectConvolutionLayer conv;
conv.configure(&src, &weights, &bias, &dst, pad_info);
// allocate tensors
src.allocator()->allocate();
weights.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
// To demonstrate how to fill tensor with some values...
src.map();
Window window;
window.use_tensor_dimensions(src_shape);
Iterator it(&src, window);
execute_window_loop(window, [&](const Coordinates & id)
{
*reinterpret_cast<half_float::half *>(it.ptr()) = half_float::half(1.f);
});
src.unmap();
// run the layer
conv.run();
// check result
dst.map();
// do something
dst.unmap();
}
/** Main program for directconvolution test
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments
*/
int main(int argc, char **argv)
{
return utils::run_example(argc, argv, main_gc_dc);
}
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