/*
 * Copyright (c) 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/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 YOLOv3 network using the Compute Library's graph API */
class GraphYOLOv3Example : public Example
{
public:
    GraphYOLOv3Example()
        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3")
    {
    }
    bool do_setup(int argc, char **argv) override
    {
        // Parse arguments
        cmd_parser.parse(argc, argv);

        // 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;
        }

        // Checks
        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");

        // 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
        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f);

        // Create input descriptor
        const TensorShape tensor_shape     = permute_shape(TensorShape(608U, 608U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_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), false))
              // Layer 1
              << ConvolutionLayer(
                  3U, 3U, 32U,
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(1, 1, 1, 1))
              .set_name("conv2d_1")
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_beta.npy"),
                  0.000001f)
              .set_name("conv2d_1/BatchNorm")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_1/LeakyRelu")

              // Layer 2
              << ConvolutionLayer(
                  3U, 3U, 64U,
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(2, 2, 1, 1))
              .set_name("conv2d_2")
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_beta.npy"),
                  0.000001f)
              .set_name("conv2d_2/BatchNorm")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_2/LeakyRelu");
        darknet53_block(data_path, "3", weights_layout, 32U);
        graph << ConvolutionLayer(
                  3U, 3U, 128U,
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(2, 2, 1, 1))
              .set_name("conv2d_5")
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_beta.npy"),
                  0.000001f)
              .set_name("conv2d_5/BatchNorm")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_5/LeakyRelu");
        darknet53_block(data_path, "6", weights_layout, 64U);
        darknet53_block(data_path, "8", weights_layout, 64U);
        graph << ConvolutionLayer(
                  3U, 3U, 256U,
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(2, 2, 1, 1))
              .set_name("conv2d_10")
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_beta.npy"),
                  0.000001f)
              .set_name("conv2d_10/BatchNorm")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_10/LeakyRelu");
        darknet53_block(data_path, "11", weights_layout, 128U);
        darknet53_block(data_path, "13", weights_layout, 128U);
        darknet53_block(data_path, "15", weights_layout, 128U);
        darknet53_block(data_path, "17", weights_layout, 128U);
        darknet53_block(data_path, "19", weights_layout, 128U);
        darknet53_block(data_path, "21", weights_layout, 128U);
        darknet53_block(data_path, "23", weights_layout, 128U);
        darknet53_block(data_path, "25", weights_layout, 128U);
        graph << ConvolutionLayer(
                  3U, 3U, 512U,
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(2, 2, 1, 1))
              .set_name("conv2d_27")
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_beta.npy"),
                  0.000001f)
              .set_name("conv2d_27/BatchNorm")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_27/LeakyRelu");
        darknet53_block(data_path, "28", weights_layout, 256U);
        darknet53_block(data_path, "30", weights_layout, 256U);
        darknet53_block(data_path, "32", weights_layout, 256U);
        darknet53_block(data_path, "34", weights_layout, 256U);
        darknet53_block(data_path, "36", weights_layout, 256U);
        darknet53_block(data_path, "38", weights_layout, 256U);
        darknet53_block(data_path, "40", weights_layout, 256U);
        darknet53_block(data_path, "42", weights_layout, 256U);
        graph << ConvolutionLayer(
                  3U, 3U, 1024U,
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(2, 2, 1, 1))
              .set_name("conv2d_44")
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"),
                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_beta.npy"),
                  0.000001f)
              .set_name("conv2d_44/BatchNorm")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_44/LeakyRelu");
        darknet53_block(data_path, "45", weights_layout, 512U);
        darknet53_block(data_path, "47", weights_layout, 512U);
        darknet53_block(data_path, "49", weights_layout, 512U);
        darknet53_block(data_path, "51", weights_layout, 512U);
        graph << 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_file  = common_params.tuner_file;

        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;

    void darknet53_block(const std::string &data_path, std::string &&param_path, DataLayout weights_layout,
                         unsigned int filter_size)
    {
        std::string total_path  = "/cnn_data/yolov3_model/";
        std::string param_path2 = arm_compute::support::cpp11::to_string(arm_compute::support::cpp11::stoi(param_path) + 1);
        SubStream   i_a(graph);
        SubStream   i_b(graph);
        i_a << ConvolutionLayer(
                1U, 1U, filter_size,
                get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout),
                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                PadStrideInfo(1, 1, 0, 0))
            << BatchNormalizationLayer(
                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"),
                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"),
                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"),
                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"),
                0.000001f)
            .set_name("conv2d" + param_path + "/BatchNorm")
            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d" + param_path + "/LeakyRelu")
            << ConvolutionLayer(
                3U, 3U, filter_size * 2,
                get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout),
                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                PadStrideInfo(1, 1, 1, 1))
            << BatchNormalizationLayer(
                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"),
                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"),
                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"),
                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"),
                0.000001f)
            .set_name("conv2d" + param_path2 + "/BatchNorm")
            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d" + param_path2 + "/LeakyRelu");

        graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add);
    }
};

/** Main program for YOLOv3
 *
 * @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
 *
 * @return Return code
 */
int main(int argc, char **argv)
{
    return arm_compute::utils::run_example<GraphYOLOv3Example>(argc, argv);
}