cpp-package/example/inception_bn.cpp

/*!
 * Copyright (c) 2016 by Contributors
 */
#include <iostream>
#include <map>
#include <string>
#include <vector>
#include "mxnet-cpp/MxNetCpp.h"
// Allow IDE to parse the types
#include "../include/mxnet-cpp/op.h"

using namespace mxnet::cpp;

static const Symbol BN_BETA;
static const Symbol BN_GAMMA;

Symbol ConvFactoryBN(Symbol data, int num_filter,
                     Shape kernel, Shape stride, Shape pad,
                     const std::string & name,
                     const std::string & suffix = "") {
  Symbol conv_w("conv_" + name + suffix + "_w"), conv_b("conv_" + name + suffix + "_b");

  Symbol conv = Convolution("conv_" + name + suffix, data,
                            conv_w, conv_b, kernel,
                            num_filter, stride, Shape(1, 1), pad);
  Symbol bn = BatchNorm("bn_" + name + suffix, conv, BN_GAMMA, BN_BETA);
  return Activation("relu_" + name + suffix, bn, "relu");
}

Symbol InceptionFactoryA(Symbol data, int num_1x1, int num_3x3red,
                         int num_3x3, int num_d3x3red, int num_d3x3,
                         PoolingPoolType pool, int proj,
                         const std::string & name) {
  Symbol c1x1 = ConvFactoryBN(data, num_1x1, Shape(1, 1), Shape(1, 1),
                              Shape(0, 0), name + "1x1");
  Symbol c3x3r = ConvFactoryBN(data, num_3x3red, Shape(1, 1), Shape(1, 1),
                               Shape(0, 0), name + "_3x3r");
  Symbol c3x3 = ConvFactoryBN(c3x3r, num_3x3, Shape(3, 3), Shape(1, 1),
                              Shape(1, 1), name + "_3x3");
  Symbol cd3x3r = ConvFactoryBN(data, num_d3x3red, Shape(1, 1), Shape(1, 1),
                                Shape(0, 0), name + "_double_3x3", "_reduce");
  Symbol cd3x3 = ConvFactoryBN(cd3x3r, num_d3x3, Shape(3, 3), Shape(1, 1),
                               Shape(1, 1), name + "_double_3x3_0");
  cd3x3 = ConvFactoryBN(data = cd3x3, num_d3x3, Shape(3, 3), Shape(1, 1),
                        Shape(1, 1), name + "_double_3x3_1");
  Symbol pooling = Pooling(name + "_pool", data,
                           Shape(3, 3), pool, false, false,
                           PoolingPoolingConvention::valid,
                           Shape(1, 1), Shape(1, 1));
  Symbol cproj = ConvFactoryBN(pooling, proj, Shape(1, 1), Shape(1, 1),
                               Shape(0, 0), name + "_proj");
  std::vector<Symbol> lst;
  lst.push_back(c1x1);
  lst.push_back(c3x3);
  lst.push_back(cd3x3);
  lst.push_back(cproj);
  return Concat("ch_concat_" + name + "_chconcat", lst, lst.size());
}

Symbol InceptionFactoryB(Symbol data, int num_3x3red, int num_3x3,
                         int num_d3x3red, int num_d3x3, const std::string & name) {
  Symbol c3x3r = ConvFactoryBN(data, num_3x3red, Shape(1, 1),
                               Shape(1, 1), Shape(0, 0),
                               name + "_3x3", "_reduce");
  Symbol c3x3 = ConvFactoryBN(c3x3r, num_3x3, Shape(3, 3), Shape(2, 2),
                              Shape(1, 1), name + "_3x3");
  Symbol cd3x3r = ConvFactoryBN(data, num_d3x3red, Shape(1, 1), Shape(1, 1),
                                Shape(0, 0), name + "_double_3x3", "_reduce");
  Symbol cd3x3 = ConvFactoryBN(cd3x3r, num_d3x3, Shape(3, 3), Shape(1, 1),
                               Shape(1, 1), name + "_double_3x3_0");
  cd3x3 = ConvFactoryBN(cd3x3, num_d3x3, Shape(3, 3), Shape(2, 2),
                        Shape(1, 1), name + "_double_3x3_1");
  Symbol pooling = Pooling("max_pool_" + name + "_pool", data,
                           Shape(3, 3), PoolingPoolType::max,
                           false, false, PoolingPoolingConvention::valid, Shape(2, 2));
  std::vector<Symbol> lst;
  lst.push_back(c3x3);
  lst.push_back(cd3x3);
  lst.push_back(pooling);
  return Concat("ch_concat_" + name + "_chconcat", lst, lst.size());
}

Symbol InceptionSymbol(int num_classes) {
  // data and label
  Symbol data = Symbol::Variable("data");
  Symbol data_label = Symbol::Variable("data_label");

  // stage 1
  Symbol conv1 = ConvFactoryBN(data, 64, Shape(7, 7), Shape(2, 2), Shape(3, 3), "conv1");
  Symbol pool1 = Pooling("pool1", conv1, Shape(3, 3), PoolingPoolType::max,
      false, false, PoolingPoolingConvention::valid, Shape(2, 2));

  // stage 2
  Symbol conv2red = ConvFactoryBN(pool1, 64, Shape(1, 1), Shape(1, 1),  Shape(0, 0), "conv2red");
  Symbol conv2 = ConvFactoryBN(conv2red, 192, Shape(3, 3), Shape(1, 1), Shape(1, 1), "conv2");
  Symbol pool2 = Pooling("pool2", conv2, Shape(3, 3), PoolingPoolType::max,
      false, false, PoolingPoolingConvention::valid, Shape(2, 2));

  // stage 3
  Symbol in3a = InceptionFactoryA(pool2, 64, 64, 64, 64, 96, PoolingPoolType::avg, 32, "3a");
  Symbol in3b = InceptionFactoryA(in3a, 64, 64, 96, 64, 96, PoolingPoolType::avg, 64, "3b");
  Symbol in3c = InceptionFactoryB(in3b, 128, 160, 64, 96, "3c");

  // stage 4
  Symbol in4a = InceptionFactoryA(in3c, 224, 64, 96, 96, 128, PoolingPoolType::avg, 128, "4a");
  Symbol in4b = InceptionFactoryA(in4a, 192, 96, 128, 96, 128,  PoolingPoolType::avg, 128, "4b");
  Symbol in4c = InceptionFactoryA(in4b, 160, 128, 160, 128, 160, PoolingPoolType::avg, 128, "4c");
  Symbol in4d = InceptionFactoryA(in4c, 96, 128, 192, 160, 192,  PoolingPoolType::avg, 128, "4d");
  Symbol in4e = InceptionFactoryB(in4d, 128, 192, 192, 256, "4e");

  // stage 5
  Symbol in5a = InceptionFactoryA(in4e, 352, 192, 320, 160, 224, PoolingPoolType::avg, 128, "5a");
  Symbol in5b = InceptionFactoryA(in5a, 352, 192, 320, 192, 224, PoolingPoolType::max, 128, "5b");

  // average pooling
  Symbol avg = Pooling("global_pool", in5b, Shape(7, 7), PoolingPoolType::avg);

  // classifier
  Symbol flatten = Flatten("flatten", avg);
  Symbol conv1_w("conv1_w"), conv1_b("conv1_b");
  Symbol fc1 = FullyConnected("fc1", flatten, conv1_w, conv1_b, num_classes);
  return SoftmaxOutput("softmax", fc1, data_label);
}

int main(int argc, char const *argv[]) {
  int batch_size = 40;
  int max_epoch = 100;
  float learning_rate = 1e-4;
  float weight_decay = 1e-4;

  auto inception_bn_net = InceptionSymbol(10);
  std::map<std::string, NDArray> args_map;
  std::map<std::string, NDArray> aux_map;

  args_map["data"] = NDArray(Shape(batch_size, 3, 224, 224), Context::gpu());
  args_map["data_label"] = NDArray(Shape(batch_size), Context::gpu());
  inception_bn_net.InferArgsMap(Context::gpu(), &args_map, args_map);

  auto train_iter = MXDataIter("ImageRecordIter")
      .SetParam("path_imglist", "./train.lst")
      .SetParam("path_imgrec", "./train.rec")
      .SetParam("data_shape", Shape(3, 224, 224))
      .SetParam("batch_size", batch_size)
      .SetParam("shuffle", 1)
      .CreateDataIter();

  auto val_iter = MXDataIter("ImageRecordIter")
      .SetParam("path_imglist", "./val.lst")
      .SetParam("path_imgrec", "./val.rec")
      .SetParam("data_shape", Shape(3, 224, 224))
      .SetParam("batch_size", batch_size)
      .CreateDataIter();

  Optimizer* opt = OptimizerRegistry::Find("ccsgd");
  opt->SetParam("momentum", 0.9)
     ->SetParam("rescale_grad", 1.0 / batch_size)
     ->SetParam("clip_gradient", 10);

  auto *exec = inception_bn_net.SimpleBind(Context::gpu(), args_map);

  for (int iter = 0; iter < max_epoch; ++iter) {
    LG << "Epoch: " << iter;
    train_iter.Reset();
    while (train_iter.Next()) {
      auto data_batch = train_iter.GetDataBatch();
      data_batch.data.CopyTo(&args_map["data"]);
      data_batch.label.CopyTo(&args_map["data_label"]);
      NDArray::WaitAll();

      exec->Forward(true);
      exec->Backward();
      exec->UpdateAll(opt, learning_rate, weight_decay);
      NDArray::WaitAll();
    }

    Accuracy acu;
    val_iter.Reset();
    while (val_iter.Next()) {
      auto data_batch = val_iter.GetDataBatch();
      data_batch.data.CopyTo(&args_map["data"]);
      data_batch.label.CopyTo(&args_map["data_label"]);
      NDArray::WaitAll();
      exec->Forward(false);
      NDArray::WaitAll();
      acu.Update(data_batch.label, exec->outputs[0]);
    }
    LG << "Accuracy: " << acu.Get();
  }
  delete exec;
  MXNotifyShutdown();
  return 0;
}