examples/graph_lenet.cpp

/*
 * Copyright (c) 2017-2018 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
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 * 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
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#include "arm_compute/graph2.h"

#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"

#include <cstdlib>

using namespace arm_compute::utils;
using namespace arm_compute::graph2::frontend;
using namespace arm_compute::graph_utils;

/** Example demonstrating how to implement LeNet'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), [optional] Path to the weights folder, [optional] batches )
 */
class GraphLenetExample : public Example
{
public:
    void do_setup(int argc, char **argv) override
    {
        std::string  data_path;   /** Path to the trainable data */
        unsigned int batches = 4; /** Number of batches */

        // 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_hint2(target);
        bool      enable_tuning            = (target == 2);
        bool      enable_memory_management = true;

        // Parse arguments
        if(argc < 2)
        {
            // Print help
            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\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] [batches]\n\n";
            std::cout << "No data folder provided: using random values\n\n";
        }
        else if(argc == 3)
        {
            //Do something with argv[1]
            data_path = argv[2];
            std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
            std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
        }
        else
        {
            //Do something with argv[1] and argv[2]
            data_path = argv[2];
            batches   = std::strtol(argv[3], nullptr, 0);
        }

        //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
        graph << target_hint
              << InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), DataType::F32), get_input_accessor(""))
              << ConvolutionLayer(
                  5U, 5U, 20U,
                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"),
                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
                  PadStrideInfo(1, 1, 0, 0))
              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
              << ConvolutionLayer(
                  5U, 5U, 50U,
                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"),
                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
                  PadStrideInfo(1, 1, 0, 0))
              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
              << FullyConnectedLayer(
                  500U,
                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"),
                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << FullyConnectedLayer(
                  10U,
                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"),
                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
              << SoftmaxLayer()
              << OutputLayer(get_output_accessor(""));

        // Finalize graph
        graph.finalize(target_hint, enable_tuning, enable_memory_management);
    }
    void do_run() override
    {
        // Run graph
        graph.run();
    }

private:
    Stream graph{ 0, "LeNet" };
};

/** Main program for LeNet
 *
 * @param[in] argc Number of arguments
 * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches )
 */
int main(int argc, char **argv)
{
    return arm_compute::utils::run_example<GraphLenetExample>(argc, argv);
}