batch scoring in cpp

Iniziate riproducendo i due percorsi descritti nell’issue con i file dei simboli e dei parametri ResNet, quindi tracciate le chiamate C++ NDArray::Load, Symbol::SimpleBind e di configurazione dell’executor utilizzate da ciascun metodo. Confrontate le mappe degli argomenti risultanti e la gestione dei batch, e definite come completato l’identificare la causa del segmentation fault e il documentare quale approccio di batch-scoring è valido e perché.

I am trying to load a pre-built model and do batch scoring, I tried the following two ways, the first one can produce some results, and the second one compiles successfully but runs with segmentation fault. Could you please let me know which way I should follow, and why the second way doesn't work? Thanks!
method 1:

/* Image size and channels */
int width = 224;
int height = 224;
int channels = 3;
int batch_size = 5;

Context ctx_dev(DeviceType::kCPU, 0);
map<string, NDArray> args_map;
map<string, NDArray> aux_map;
map<string, NDArray> parameters;

NDArray::Load("../Resnet/resnet-152-0000.params", 0, &parameters);

for (const auto &k : parameters) {
  if (k.first.substr(0, 4) == "aux:") {
    auto name = k.first.substr(4, k.first.size() - 4);
    aux_map[name] = k.second.Copy(ctx_dev);
  }
  if (k.first.substr(0, 4) == "arg:") {
    auto name = k.first.substr(4, k.first.size() - 4);
    args_map[name] = k.second.Copy(ctx_dev);
  }
}

auto net = Symbol::Load("../Resnet/resnet-152-symbol.json");

auto data_iter = MXDataIter("ImageRecordIter")
.SetParam("path_imglist","../caltech_256/caltech-256-60-train.lst")
.SetParam("path_imgrec","../caltech_256/caltech-256-60-train.rec")
.SetParam("data_shape", Shape(3, 224, 224))
.SetParam("batch_size", batch_size)
.SetParam("shuffle", 1)
.CreateDataIter();

while(data_iter.Next()){
auto batch = data_iter.GetData();
args_map["data"] = batch;
auto *exec = net.SimpleBind(ctx_dev, args_map);
exec->Forward(false);
auto outputs = exec->outputs[0].Copy(Context(kCPU, 0));
NDArray::WaitAll();
for (int i = 0; i <2; i++) {
cout << outputs.At(0, i) <<",";
}
cout << endl;
}
MXNotifyShutdown();

method 2:

int width = 224;
int height = 224;
int channels = 3;
int batch_size = 5;

Context ctx_dev(DeviceType::kCPU, 0);

map<string, NDArray> args_map;
map<string, NDArray> aux_map;

args_map["data"] = NDArray(Shape(batch_size, channels, width, height), ctx_dev);
args_map["label"] = NDArray(Shape(batch_size), ctx_dev);

auto net = Symbol::Load("../Resnet/resnet-152-symbol.json");

auto *exec = net.SimpleBind(ctx_dev, args_map);

NDArray::Load("../Resnet/resnet-152-0000.params", 0, &args_map);

auto data_iter = MXDataIter("ImageRecordIter")
  .SetParam("path_imglist","../caltech_256/caltech-256-60-train.lst")
  .SetParam("path_imgrec","../caltech_256/caltech-256-60-train.rec")
  .SetParam("data_shape", Shape(3, 224, 224))
  .SetParam("batch_size", batch_size)
  .SetParam("shuffle", 1)
  .CreateDataIter();

while(data_iter.Next()){
auto batch = data_iter.GetDataBatch();
batch.data.CopyTo(&args_map["data"]);
batch.label.CopyTo(&args_map["label"]);
exec->Forward(false);
NDArray::WaitAll();
}

delete exec;
MXNotifyShutdown();