examples/gc_dc.cpp

   1 /*
   2  * Copyright (c) 2017, 2018 ARM Limited.
   3  *
   4  * SPDX-License-Identifier: MIT
   5  *
   6  * Permission is hereby granted, free of charge, to any person obtaining a copy
   7  * of this software and associated documentation files (the "Software"), to
   8  * deal in the Software without restriction, including without limitation the
   9  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  10  * sell copies of the Software, and to permit persons to whom the Software is
  11  * furnished to do so, subject to the following conditions:
  12  *
  13  * The above copyright notice and this permission notice shall be included in all
  14  * copies or substantial portions of the Software.
  15  *
  16  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  17  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  18  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  19  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  20  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  21  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  22  * SOFTWARE.
  23  */
  24 #ifndef ARM_COMPUTE_GC
  25 #error "This example needs to be built with -DARM_COMPUTE_GC"
  26 #endif /* ARM_COMPUTE_GC */
  27
  28 #include "arm_compute/runtime/GLES_COMPUTE/GCFunctions.h"
  29 #include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
  30 #include "half/half.hpp"
  31 #include "utils/Utils.h"
  32
  33 using namespace arm_compute;
  34 using namespace utils;
  35
  36 class GCDCExample : public Example
  37 {
  38 public:
  39     void do_setup(int argc, char **argv) override
  40     {
  41         ARM_COMPUTE_UNUSED(argc);
  42         ARM_COMPUTE_UNUSED(argv);
  43
  44         // init instance
  45         GCScheduler::get().default_init();
  46
  47         const TensorShape  src_shape   = TensorShape{ 11U /* W */, 13U /* H */, 4U /* C */, 3U /* N */ };
  48         const unsigned int kernel_size = 3;
  49         const int          stride_x    = 1;
  50         const int          stride_y    = 1;
  51         const int          pad_x       = 0;
  52         const int          pad_y       = 0;
  53         const unsigned int num_kernels = 256;
  54         const DataType     data_type   = DataType::F16;
  55
  56         // generate shape
  57         const TensorShape   weights_shape(kernel_size, kernel_size, src_shape.z(), num_kernels);
  58         const TensorShape   bias_shape(num_kernels);
  59         const PadStrideInfo pad_info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
  60
  61         // output shape should be 9*11*256*3 (W*H*C*N)
  62         const TensorShape dst_shape = get_output_shape(src_shape, weights_shape, pad_info);
  63
  64         // create tensors
  65         src.allocator()->init(TensorInfo(src_shape, 1, data_type));
  66         weights.allocator()->init(TensorInfo(weights_shape, 1, data_type));
  67         bias.allocator()->init(TensorInfo(bias_shape, 1, data_type));
  68         dst.allocator()->init(TensorInfo(dst_shape, 1, data_type));
  69
  70         // configure layer
  71         conv.configure(&src, &weights, &bias, &dst, pad_info);
  72
  73         // allocate tensors
  74         src.allocator()->allocate();
  75         weights.allocator()->allocate();
  76         bias.allocator()->allocate();
  77         dst.allocator()->allocate();
  78
  79         // To demonstrate how to fill tensor with some values...
  80         src.map();
  81         Window window;
  82         window.use_tensor_dimensions(src_shape);
  83         Iterator it(&src, window);
  84         execute_window_loop(window, [&](const Coordinates & id)
  85         {
  86             *reinterpret_cast<half_float::half *>(it.ptr()) = half_float::half(1.f);
  87         });
  88         src.unmap();
  89     }
  90     void do_run() override
  91     {
  92         // run the layer
  93         conv.run();
  94     }
  95     void do_teardown() override
  96     {
  97         // check result
  98         dst.map();
  99         // do something
 100         dst.unmap();
 101     }
 102
 103 private:
 104     GCTensor                 src{}, weights{}, bias{}, dst{};
 105     GCDirectConvolutionLayer conv{};
 106
 107     TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &info)
 108     {
 109         TensorShape out_shape(in_shape);
 110         const std::pair<unsigned int, unsigned int> scaled_dims = scaled_dimensions(in_shape.x(),
 111                                                                                     in_shape.y(),
 112                                                                                     kernel_shape.x(),
 113                                                                                     kernel_shape.y(),
 114                                                                                     info);
 115         out_shape.set(0, scaled_dims.first);
 116         out_shape.set(1, scaled_dims.second);
 117         out_shape.set(2, kernel_shape[3]);
 118         return out_shape;
 119     }
 120 };
 121
 122 /** Main program for directconvolution test
 123  *
 124  * @param[in] argc Number of arguments
 125  * @param[in] argv Arguments
 126  */
 127 int main(int argc, char **argv)
 128 {
 129     return utils::run_example<GCDCExample>(argc, argv);
 130 }