Caffe Segmentation fault

Caffe Segmentation fault

[root@cobalt googlenet_places205]# cd ..
[root@cobalt models]# caffe train -solver $SOLVER_PROTO -weights $INITIAL_WEIGHTS
caffe: /root/anaconda/lib/libtiff.so.5: no version information available (required by /usr/local/lib/libopencv_imgcodecs.so.3.0)
F0530 17:51:00.062386  8965 io.cpp:34] Check failed: fd != -1 (-1 vs. -1) File not found: models/unary_depth_regressor/solver_softmax_dir.prototxt
*** Check failure stack trace: ***
    @     0x7f37ddd20e6d  (unknown)
    @     0x7f37ddd22ced  (unknown)
    @     0x7f37ddd20a5c  (unknown)
    @     0x7f37ddd2363e  (unknown)
    @     0x7f37e202ca3d  caffe::ReadProtoFromTextFile()
    @           0x407182  train()
    @           0x405871  main
    @     0x7f37d90b0af5  __libc_start_main
    @           0x405e1d  (unknown)
Aborted
[root@cobalt models]# cd ..
[root@cobalt DepthPrediction]# caffe train -solver $SOLVER_PROTO -weights $INITIAL_WEIGHTS
caffe: /root/anaconda/lib/libtiff.so.5: no version information available (required by /usr/local/lib/libopencv_imgcodecs.so.3.0)
I0530 17:51:37.176177  8972 caffe.cpp:117] Use CPU.
I0530 17:51:37.176739  8972 caffe.cpp:121] Starting Optimization
I0530 17:51:37.176849  8972 solver.cpp:32] Initializing solver from parameters: 
test_iter: 100
test_interval: 1000
base_lr: 0.001
display: 20
max_iter: 40000
lr_policy: "step"
gamma: 0.5
momentum: 0.9
weight_decay: 0.0005
stepsize: 5000
snapshot: 1000
snapshot_prefix: "models/unary_depth_regressor/snapshots_softmax/softmax_centerdepth"
solver_mode: CPU
net: "models/unary_depth_regressor/udr_softmax_dir.prototxt"
I0530 17:51:37.176964  8972 solver.cpp:70] Creating training net from net file: models/unary_depth_regressor/udr_softmax_dir.prototxt
E0530 17:51:37.177558  8972 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: models/unary_depth_regressor/udr_softmax_dir.prototxt
I0530 17:51:37.190326  8972 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter
I0530 17:51:37.190466  8972 net.cpp:287] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0530 17:51:37.190524  8972 net.cpp:287] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0530 17:51:37.190803  8972 net.cpp:42] Initializing net from parameters: 
name: "Unary depth regressor, softmax version: directory input. Center depth values:"
state {
  phase: TRAIN
}
layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "train/NYUv2/train_full_167_v3/mean.binaryproto"
  }
  image_data_param {
    source: "train/NYUv2/train_full_167_v3/index_16_norm_randomized.txt"
    batch_size: 128
    shuffle: true
    new_height: 227
    new_width: 227
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 0
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc_n1"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc_n1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  inner_product_param {
    num_output: 128
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu_n1"
  type: "ReLU"
  bottom: "fc_n1"
  top: "fc_n1"
}
layer {
  name: "drop_n1"
  type: "Dropout"
  bottom: "fc_n1"
  top: "fc_n1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc_n2"
  type: "InnerProduct"
  bottom: "fc_n1"
  top: "fc_n2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  inner_product_param {
    num_output: 16
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prob"
  type: "SoftmaxWithLoss"
  bottom: "fc_n2"
  bottom: "label"
  top: "prob"
}
I0530 17:51:37.193035  8972 layer_factory.hpp:74] Creating layer data
I0530 17:51:37.193068  8972 net.cpp:90] Creating Layer data
I0530 17:51:37.193087  8972 net.cpp:368] data -> data
I0530 17:51:37.193122  8972 net.cpp:368] data -> label
I0530 17:51:37.193150  8972 net.cpp:120] Setting up data
I0530 17:51:37.193169  8972 image_data_layer.cpp:36] Opening file train/NYUv2/train_full_167_v3/index_16_norm_randomized.txt
I0530 17:51:37.206234  8972 image_data_layer.cpp:46] Shuffling data
I0530 17:51:37.206730  8972 image_data_layer.cpp:51] A total of 0 images.
*** Aborted at 1432979497 (unix time) try "date -d @1432979497" if you are using GNU date ***
PC: @     0x7fc81cb2a9b0 (unknown)
*** SIGSEGV (@0x0) received by PID 8972 (TID 0x7fc822d10940) from PID 0; stack trace: ***
    @     0x7fc819d75130 (unknown)
    @     0x7fc81cb2a9b0 (unknown)
    @     0x7fc8228ccf3c std::operator+<>()
    @     0x7fc8228cd905 caffe::ImageDataLayer<>::DataLayerSetUp()
    @     0x7fc8228fced6 caffe::BaseDataLayer<>::LayerSetUp()
    @     0x7fc8228fcfd9 caffe::BasePrefetchingDataLayer<>::LayerSetUp()
    @     0x7fc82296cea3 caffe::Net<>::Init()
    @     0x7fc82296ec12 caffe::Net<>::Net()
    @     0x7fc822979b80 caffe::Solver<>::InitTrainNet()
    @     0x7fc82297ab23 caffe::Solver<>::Init()
    @     0x7fc82297acf6 caffe::Solver<>::Solver()
    @           0x40d250 caffe::GetSolver<>()
    @           0x407243 train()
    @           0x405871 main
    @     0x7fc8199c6af5 __libc_start_main
    @           0x405e1d (unknown)
Segmentation fault
[root@cobalt DepthPrediction]# 

That my be helpful for people who will land here in future.
I had kind of the same problem, but I was loading images from leveldb file.
And this error appeared when I copied leveldb files generated on machine A to another machine B and tried to run caffe on B.
When I regenerated leveldb once again on the machine B, problem was solved.
BTW. may be anybody knows why does the machine on which was leveldb generated matter?