text: /*
   * 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.
   */
  #include "arm_compute/graph.h"
  #include "support/ToolchainSupport.h"
  #include "utils/GraphUtils.h"
  #include "utils/Utils.h"
  
  #include <cstdlib>
  #include <tuple>
  
  using namespace arm_compute::utils;
  using namespace arm_compute::graph::frontend;
  using namespace arm_compute::graph_utils;
  
  /** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API
   *
   * @param[in] argc Number of arguments
   * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
   */
  class GraphGooglenetExample : public Example
  {
  public:
      void do_setup(int argc, char **argv) override
      {
          std::string data_path; /* Path to the trainable data */
          std::string image;     /* Image data */
          std::string label;     /* Label data */
  
          // Create a preprocessor object
          const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
          std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
  
          // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
          const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
          Target       target_hint    = set_target_hint(target);
          FastMathHint fast_math_hint = FastMathHint::DISABLED;
  
          // Parse arguments
          if(argc < 2)
          {
              // Print help
              std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
              std::cout << "No data folder provided: using random values\n\n";
          }
          else if(argc == 2)
          {
              std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
              std::cout << "No data folder provided: using random values\n\n";
          }
          else if(argc == 3)
          {
              data_path = argv[2];
              std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
              std::cout << "No image provided: using random values\n\n";
          }
          else if(argc == 4)
          {
              data_path = argv[2];
              image     = argv[3];
              std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
              std::cout << "No text file with labels provided: skipping output accessor\n\n";
          }
          else if(argc == 5)
          {
              data_path = argv[2];
              image     = argv[3];
              label     = argv[4];
              std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
              std::cout << "No fast math info provided: disabling fast math\n\n";
          }
          else
          {
              data_path      = argv[2];
              image          = argv[3];
              label          = argv[4];
              fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
          }
  
          graph << target_hint
                << fast_math_hint
                << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
                              get_input_accessor(image, std::move(preprocessor)))
                << ConvolutionLayer(
                    7U, 7U, 64U,
                    get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
                    get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
                    PadStrideInfo(2, 2, 3, 3))
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
                << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
                << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
                << ConvolutionLayer(
                    1U, 1U, 64U,
                    get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"),
                    get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
                << ConvolutionLayer(
                    3U, 3U, 192U,
                    get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"),
                    get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
                    PadStrideInfo(1, 1, 1, 1))
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
                << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
                << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
          graph << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U);
          graph << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U);
          graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
          graph << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U);
          graph << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U);
          graph << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U);
          graph << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U);
          graph << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U);
          graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
          graph << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U);
          graph << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U);
          graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
                << FullyConnectedLayer(
                    1000U,
                    get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"),
                    get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
                << SoftmaxLayer()
                << OutputLayer(get_output_accessor(label, 5));
  
          // Finalize graph
          GraphConfig config;
          config.use_tuner = (target == 2);
          graph.finalize(target_hint, config);
      }
      void do_run() override
      {
          // Run graph
          graph.run();
      }
  
  private:
      Stream graph{ 0, "GoogleNet" };
  
      BranchLayer get_inception_node(const std::string &data_path, std::string &&param_path,
                                     unsigned int a_filt,
                                     std::tuple<unsigned int, unsigned int> b_filters,
                                     std::tuple<unsigned int, unsigned int> c_filters,
                                     unsigned int d_filt)
      {
          std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_";
          SubStream   i_a(graph);
          i_a << ConvolutionLayer(
                  1U, 1U, a_filt,
                  get_weights_accessor(data_path, total_path + "1x1_w.npy"),
                  get_weights_accessor(data_path, total_path + "1x1_b.npy"),
                  PadStrideInfo(1, 1, 0, 0))
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(
                  1U, 1U, std::get<0>(b_filters),
                  get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"),
                  get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
                  PadStrideInfo(1, 1, 0, 0))
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(
                  3U, 3U, std::get<1>(b_filters),
                  get_weights_accessor(data_path, total_path + "3x3_w.npy"),
                  get_weights_accessor(data_path, total_path + "3x3_b.npy"),
                  PadStrideInfo(1, 1, 1, 1))
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_c(graph);
          i_c << ConvolutionLayer(
                  1U, 1U, std::get<0>(c_filters),
                  get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"),
                  get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
                  PadStrideInfo(1, 1, 0, 0))
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(
                  5U, 5U, std::get<1>(c_filters),
                  get_weights_accessor(data_path, total_path + "5x5_w.npy"),
                  get_weights_accessor(data_path, total_path + "5x5_b.npy"),
                  PadStrideInfo(1, 1, 2, 2))
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_d(graph);
          i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)))
              << ConvolutionLayer(
                  1U, 1U, d_filt,
                  get_weights_accessor(data_path, total_path + "pool_proj_w.npy"),
                  get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
                  PadStrideInfo(1, 1, 0, 0))
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
      }
  };
  
  /** Main program for Googlenet
   *
   * @param[in] argc Number of arguments
   * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
   */
  int main(int argc, char **argv)
  {
      return arm_compute::utils::run_example<GraphGooglenetExample>(argc, argv);
  }
  