examples/graph_mobilenet.cpp

/*
 * Copyright (c) 2017 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/Graph.h"
#include "arm_compute/graph/Nodes.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"

#include <cstdlib>

using namespace arm_compute::graph;
using namespace arm_compute::graph_utils;

BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
                          unsigned int  conv_filt,
                          PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
{
    std::string total_path = "/cnn_data/mobilenet_v1_model/" + param_path + "_";
    SubGraph    sg;
    sg << DepthwiseConvolutionLayer(
           3U, 3U,
           get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
           std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
           dwc_pad_stride_info,
           true)
       << BatchNormalizationLayer(
           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
           0.001f)
       << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
       << ConvolutionLayer(
           1U, 1U, conv_filt,
           get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
           std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
           conv_pad_stride_info)
       << BatchNormalizationLayer(
           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
           0.001f)
       << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));

    return BranchLayer(std::move(sg));
}

/** Example demonstrating how to implement MobileNet's network using the Compute Library's graph API
 *
 * @param[in] argc Number of arguments
 * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
 */
void main_graph_mobilenet(int argc, const char **argv)
{
    std::string data_path; /* Path to the trainable data */
    std::string image;     /* Image data */
    std::string label;     /* Label data */

    constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
    constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
    constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */

    // Parse arguments
    if(argc < 2)
    {
        // Print help
        std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n";
        std::cout << "No data folder provided: using random values\n\n";
    }
    else if(argc == 2)
    {
        data_path = argv[1];
        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n";
        std::cout << "No image provided: using random values\n\n";
    }
    else if(argc == 3)
    {
        data_path = argv[1];
        image     = argv[2];
        std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [labels]\n\n";
        std::cout << "No text file with labels provided: skipping output accessor\n\n";
    }
    else
    {
        data_path = argv[1];
        image     = argv[2];
        label     = argv[3];
    }

    // Check if OpenCL is available and initialize the scheduler
    TargetHint hint = TargetHint::NEON;
    if(Graph::opencl_is_available())
    {
        hint = TargetHint::OPENCL;
    }

    Graph graph;
    graph << hint
          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
                    get_input_accessor(image, mean_r, mean_g, mean_b))
          << ConvolutionLayer(
              3U, 3U, 32U,
              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_weights.npy"),
              std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
              PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
          << BatchNormalizationLayer(
              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_mean.npy"),
              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_variance.npy"),
              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_beta.npy"),
              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_gamma.npy"),
              0.001f)
          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
          << get_dwsc_node(data_path, "Conv2d_1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_2", 128, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_3", 128, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_4", 256, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_5", 256, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_6", 512, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_7", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_8", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_9", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_10", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_11", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_12", 1024, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << get_dwsc_node(data_path, "Conv2d_13", 1024, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
          << ConvolutionLayer(
              1U, 1U, 1001U,
              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_weights.npy"),
              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_biases.npy"),
              PadStrideInfo(1, 1, 0, 0))
          << ReshapeLayer(TensorShape(1001U))
          << SoftmaxLayer()
          << Tensor(get_output_accessor(label, 5));

    graph.run();
}

/** Main program for MobileNetV1
 *
 * @param[in] argc Number of arguments
 * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
 */
int main(int argc, const char **argv)
{
    return arm_compute::utils::run_example(argc, argv, main_graph_mobilenet);
}