text: /*
   * Copyright (c) 2017-2021 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/CommonGraphOptions.h"
  #include "utils/GraphUtils.h"
  #include "utils/Utils.h"
  
  using namespace arm_compute::utils;
  using namespace arm_compute::graph::frontend;
  using namespace arm_compute::graph_utils;
  
  /** Example demonstrating how to implement Squeezenet's network using the Compute Library's graph API */
  class GraphSqueezenetExample : public Example
  {
  public:
      GraphSqueezenetExample()
          : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "SqueezeNetV1")
      {
      }
      bool do_setup(int argc, char **argv) override
      {
          // Parse arguments
          cmd_parser.parse(argc, argv);
          cmd_parser.validate();
  
          // Consume common parameters
          common_params = consume_common_graph_parameters(common_opts);
  
          // Return when help menu is requested
          if(common_params.help)
          {
              cmd_parser.print_help(argv[0]);
              return false;
          }
  
          // Print parameter values
          std::cout << common_params << std::endl;
  
          // Get trainable parameters data path
          std::string data_path = common_params.data_path;
  
          // Create a preprocessor object
          const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
          std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
  
          // Create input descriptor
          const auto        operation_layout = common_params.data_layout;
          const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
          TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
  
          // Set weights trained layout
          const DataLayout weights_layout = DataLayout::NCHW;
  
          graph << common_params.target
                << common_params.fast_math_hint
                << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
                << ConvolutionLayer(
                    7U, 7U, 96U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
                    PadStrideInfo(2, 2, 0, 0))
                .set_name("conv1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1")
                << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
                << ConvolutionLayer(
                    1U, 1U, 16U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("fire2/squeeze1x1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire2/relu_squeeze1x1");
          graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U).set_name("fire2/concat");
          graph << ConvolutionLayer(
                    1U, 1U, 16U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("fire3/squeeze1x1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire3/relu_squeeze1x1");
          graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U).set_name("fire3/concat");
          graph << ConvolutionLayer(
                    1U, 1U, 32U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("fire4/squeeze1x1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire4/relu_squeeze1x1");
          graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U).set_name("fire4/concat");
          graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4")
                << ConvolutionLayer(
                    1U, 1U, 32U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("fire5/squeeze1x1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire5/relu_squeeze1x1");
          graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U).set_name("fire5/concat");
          graph << ConvolutionLayer(
                    1U, 1U, 48U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("fire6/squeeze1x1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire6/relu_squeeze1x1");
          graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U).set_name("fire6/concat");
          graph << ConvolutionLayer(
                    1U, 1U, 48U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("fire7/squeeze1x1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire7/relu_squeeze1x1");
          graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U).set_name("fire7/concat");
          graph << ConvolutionLayer(
                    1U, 1U, 64U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("fire8/squeeze1x1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire8/relu_squeeze1x1");
          graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U).set_name("fire8/concat");
          graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool8")
                << ConvolutionLayer(
                    1U, 1U, 64U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("fire9/squeeze1x1")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire9/relu_squeeze1x1");
          graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U).set_name("fire9/concat");
          graph << ConvolutionLayer(
                    1U, 1U, 1000U,
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
                    PadStrideInfo(1, 1, 0, 0))
                .set_name("conv10")
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10")
                << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool10")
                << FlattenLayer().set_name("flatten")
                << SoftmaxLayer().set_name("prob")
                << OutputLayer(get_output_accessor(common_params, 5));
  
          // Finalize graph
          GraphConfig config;
          config.num_threads        = common_params.threads;
          config.use_tuner          = common_params.enable_tuner;
          config.tuner_mode         = common_params.tuner_mode;
          config.tuner_file         = common_params.tuner_file;
          config.mlgo_file          = common_params.mlgo_file;
          config.use_synthetic_type = arm_compute::is_data_type_quantized(common_params.data_type);
          config.synthetic_type     = common_params.data_type;
  
          graph.finalize(common_params.target, config);
  
          return true;
      }
      void do_run() override
      {
          // Run graph
          graph.run();
      }
  
  private:
      CommandLineParser  cmd_parser;
      CommonGraphOptions common_opts;
      CommonGraphParams  common_params;
      Stream             graph;
  
      ConcatLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, DataLayout weights_layout,
                                       unsigned int expand1_filt, unsigned int expand3_filt)
      {
          std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";
          SubStream   i_a(graph);
          i_a << ConvolutionLayer(
                  1U, 1U, expand1_filt,
                  get_weights_accessor(data_path, total_path + "expand1x1_w.npy", weights_layout),
                  get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
                  PadStrideInfo(1, 1, 0, 0))
              .set_name(param_path + "/expand1x1")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand1x1");
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(
                  3U, 3U, expand3_filt,
                  get_weights_accessor(data_path, total_path + "expand3x3_w.npy", weights_layout),
                  get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
                  PadStrideInfo(1, 1, 1, 1))
              .set_name(param_path + "/expand3x3")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand3x3");
  
          return ConcatLayer(std::move(i_a), std::move(i_b));
      }
  };
  
  /** Main program for Squeezenet v1.0
   *
   * Model is based on:
   *      https://arxiv.org/abs/1602.07360
   *      "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
   *      Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
   *
   * Provenance: https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel
   *
   * @note To list all the possible arguments execute the binary appended with the --help option
   *
   * @param[in] argc Number of arguments
   * @param[in] argv Arguments
   */
  int main(int argc, char **argv)
  {
      return arm_compute::utils::run_example<GraphSqueezenetExample>(argc, argv);
  }
  