Part Number: TDA2EXEVM

Hi,

I follow the documentation for the semantic segmentation example. but when I do the cmd  './ train_cityscapes_segmentation.sh'. an error ‘Unknown layer type: 'ImageLabelData'’ is as follows:

Why?

layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "out_deconv_final_up8"
  bottom: "label"
  top: "loss"
  propagate_down: true
  propagate_down: false
  loss_param {
    ignore_label: 255
    normalization: VALID
  }
}
I0702 16:59:32.358427 18518 net.cpp:110] Using FLOAT as default forward math type
I0702 16:59:32.358439 18518 net.cpp:116] Using FLOAT as default backward math type
I0702 16:59:32.358448 18518 layer_factory.hpp:172] Creating layer 'data' of type 'ImageLabelData'
F0702 16:59:32.358484 18518 layer_factory.hpp:175] Check failed: registry.count(layer_type) == 1 (0 vs. 1) Unknown layer type: 'ImageLabelData' (known types: AbsVal, Accuracy, AnnotatedData, ArgMax, Axpy, BNLL, BatchNorm, BatchReindex, Bias, Concat, ContrastiveLoss, Convolution, Crop, Data, Deconvolution, DetectNetTransformation, DetectionEvaluate, DetectionOutput, Dropout, DummyData, ELU, Eltwise, Embed, EuclideanLoss, Exp, Filter, Flatten, HDF5Data, HDF5Output, HingeLoss, Im2col, ImageData, InfogainLoss, InnerProduct, Input, L1Loss, LRN, LSTM, LSTMUnit, Log, MVN, MemoryData, MultiBoxLoss, MultinomialLogisticLoss, Normalize, PReLU, Permute, Pooling, Power, PriorBox, Python, RNN, ReLU, Reduction, Reshape, SPP, Scale, SegmentationAccuracy, Sigmoid, SigmoidCrossEntropyLoss, Silence, Slice, SmoothL1Loss, Softmax, SoftmaxWithLoss, Split, TanH, Threshold, Tile, VideoData, WindowData)
*** Check failure stack trace: ***
    @     0x7f5db7c615cd  google::LogMessage::Fail()
    @     0x7f5db7c63433  google::LogMessage::SendToLog()
    @     0x7f5db7c6115b  google::LogMessage::Flush()
    @     0x7f5db7c63e1e  google::LogMessageFatal::~LogMessageFatal()
    @     0x7f5db8873f19  caffe::LayerRegistry::CreateLayer()
    @     0x7f5db8867d2e  caffe::Net::Init()
    @     0x7f5db8869cb4  caffe::Net::Net()
    @     0x7f5db880e63f  caffe::Solver::InitTrainNet()
    @     0x7f5db880ebe4  caffe::Solver::Init()
    @     0x7f5db880f084  caffe::Solver::Solver()
    @     0x7f5db8c93686  caffe::Creator_SGDSolver()
    @           0x41c696  caffe::SolverRegistry::CreateSolver()
    @           0x40f6c5  train()
    @           0x40d0e0  main
    @     0x7f5db6381830  __libc_start_main
    @           0x40dd79  _start
    @              (nil)  (unknown)