Caffe: make mattest

[hanxiaoyu]

When I try make mattest, problem appeared as:

cd matlab; /usr/local/MATLAB/R2016b/bin/matlab -nodisplay -r caffe.run_tests(), exit()

                            < M A T L A B (R) >
                  Copyright 1984-2016 The MathWorks, Inc.
                   R2016b (9.1.0.441655) 64-bit (glnxa64)
                             September 7, 2016


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Cleared 0 solvers and 0 stand-alone nets
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0320 17:58:08.510360  2344 net.cpp:51] Initializing net from parameters: 
name: "testnet"
force_backward: true
state {
  phase: TRAIN
  level: 0
}
layer {
  name: "data"
  type: "DummyData"
  top: "data"
  top: "label"
  dummy_data_param {
    data_filler {
      type: "gaussian"
      std: 1
    }
    data_filler {
      type: "constant"
    }
    num: 5
    num: 5
    channels: 2
    channels: 1
    height: 3
    height: 1
    width: 4
    width: 1
  }
}
layer {
  name: "conv"
  type: "Convolution"
  bottom: "data"
  top: "conv"
  param {
    decay_mult: 1
  }
  param {
    decay_mult: 0
  }
  convolution_param {
    num_output: 11
    pad: 3
    kernel_size: 2
    weight_filler {
      type: "gaussian"
      std: 1
    }
    bias_filler {
      type: "constant"
      value: 2
    }
  }
}
layer {
  name: "ip"
  type: "InnerProduct"
  bottom: "conv"
  top: "ip"
  inner_product_param {
    num_output: 13
    weight_filler {
      type: "gaussian"
      std: 2.5
    }
    bias_filler {
      type: "constant"
      value: -3
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip"
  bottom: "label"
  top: "loss"
}
I0320 17:58:08.510404  2344 layer_factory.hpp:77] Creating layer data
I0320 17:58:08.510412  2344 net.cpp:84] Creating Layer data
I0320 17:58:08.510417  2344 net.cpp:380] data -> data
I0320 17:58:08.510424  2344 net.cpp:380] data -> label
I0320 17:58:08.510435  2344 net.cpp:122] Setting up data
I0320 17:58:08.510442  2344 net.cpp:129] Top shape: 5 2 3 4 (120)
I0320 17:58:08.510447  2344 net.cpp:129] Top shape: 5 1 1 1 (5)
I0320 17:58:08.510448  2344 net.cpp:137] Memory required for data: 500
I0320 17:58:08.510450  2344 layer_factory.hpp:77] Creating layer conv
I0320 17:58:08.510457  2344 net.cpp:84] Creating Layer conv
I0320 17:58:08.510462  2344 net.cpp:406] conv <- data
I0320 17:58:08.510468  2344 net.cpp:380] conv -> conv
I0320 17:58:08.716840  2344 net.cpp:122] Setting up conv
I0320 17:58:08.716861  2344 net.cpp:129] Top shape: 5 11 8 9 (3960)
I0320 17:58:08.716864  2344 net.cpp:137] Memory required for data: 16340
I0320 17:58:08.716887  2344 layer_factory.hpp:77] Creating layer ip
I0320 17:58:08.716897  2344 net.cpp:84] Creating Layer ip
I0320 17:58:08.716902  2344 net.cpp:406] ip <- conv
I0320 17:58:08.716907  2344 net.cpp:380] ip -> ip
I0320 17:58:08.717002  2344 net.cpp:122] Setting up ip
I0320 17:58:08.717006  2344 net.cpp:129] Top shape: 5 13 (65)
I0320 17:58:08.717010  2344 net.cpp:137] Memory required for data: 16600
I0320 17:58:08.717013  2344 layer_factory.hpp:77] Creating layer loss
I0320 17:58:08.717017  2344 net.cpp:84] Creating Layer loss
I0320 17:58:08.717034  2344 net.cpp:406] loss <- ip
I0320 17:58:08.717036  2344 net.cpp:406] loss <- label
I0320 17:58:08.717042  2344 net.cpp:380] loss -> loss
I0320 17:58:08.717051  2344 layer_factory.hpp:77] Creating layer loss
I0320 17:58:08.717181  2344 net.cpp:122] Setting up loss
I0320 17:58:08.717187  2344 net.cpp:129] Top shape: (1)
I0320 17:58:08.717190  2344 net.cpp:132]     with loss weight 1
I0320 17:58:08.717221  2344 net.cpp:137] Memory required for data: 16604
I0320 17:58:08.717226  2344 net.cpp:198] loss needs backward computation.
I0320 17:58:08.717231  2344 net.cpp:198] ip needs backward computation.
I0320 17:58:08.717233  2344 net.cpp:198] conv needs backward computation.
I0320 17:58:08.717236  2344 net.cpp:200] data does not need backward computation.
I0320 17:58:08.717239  2344 net.cpp:242] This network produces output loss
I0320 17:58:08.717243  2344 net.cpp:255] Network initialization done.
I0320 17:58:08.743211  2344 net.cpp:51] Initializing net from parameters: 
name: "testnet"
force_backward: true
state {
  phase: TRAIN
  level: 0
}
layer {
  name: "data"
  type: "DummyData"
  top: "data"
  top: "label"
  dummy_data_param {
    data_filler {
      type: "gaussian"
      std: 1
    }
    data_filler {
      type: "constant"
    }
    num: 5
    num: 5
    channels: 2
    channels: 1
    height: 3
    height: 1
    width: 4
    width: 1
  }
}
layer {
  name: "conv"
  type: "Convolution"
  bottom: "data"
  top: "conv"
  param {
    decay_mult: 1
  }
  param {
    decay_mult: 0
  }
  convolution_param {
    num_output: 11
    pad: 3
    kernel_size: 2
    weight_filler {
      type: "gaussian"
      std: 1
    }
    bias_filler {
      type: "constant"
      value: 2
    }
  }
}
layer {
  name: "ip"
  type: "InnerProduct"
  bottom: "conv"
  top: "ip"
  inner_product_param {
    num_output: 13
    weight_filler {
      type: "gaussian"
      std: 2.5
    }
    bias_filler {
      type: "constant"
      value: -3
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip"
  bottom: "label"
  top: "loss"
}
I0320 17:58:08.743255  2344 layer_factory.hpp:77] Creating layer data
I0320 17:58:08.743268  2344 net.cpp:84] Creating Layer data
I0320 17:58:08.743275  2344 net.cpp:380] data -> data
I0320 17:58:08.743283  2344 net.cpp:380] data -> label
I0320 17:58:08.743293  2344 net.cpp:122] Setting up data
I0320 17:58:08.743296  2344 net.cpp:129] Top shape: 5 2 3 4 (120)
I0320 17:58:08.743299  2344 net.cpp:129] Top shape: 5 1 1 1 (5)
I0320 17:58:08.743302  2344 net.cpp:137] Memory required for data: 500
I0320 17:58:08.743304  2344 layer_factory.hpp:77] Creating layer conv
I0320 17:58:08.743311  2344 net.cpp:84] Creating Layer conv
I0320 17:58:08.743314  2344 net.cpp:406] conv <- data
I0320 17:58:08.743319  2344 net.cpp:380] conv -> conv
I0320 17:58:08.744292  2344 net.cpp:122] Setting up conv
I0320 17:58:08.744302  2344 net.cpp:129] Top shape: 5 11 8 9 (3960)
I0320 17:58:08.744304  2344 net.cpp:137] Memory required for data: 16340
I0320 17:58:08.744313  2344 layer_factory.hpp:77] Creating layer ip
I0320 17:58:08.744319  2344 net.cpp:84] Creating Layer ip
I0320 17:58:08.744321  2344 net.cpp:406] ip <- conv
I0320 17:58:08.744328  2344 net.cpp:380] ip -> ip
I0320 17:58:08.744418  2344 net.cpp:122] Setting up ip
I0320 17:58:08.744423  2344 net.cpp:129] Top shape: 5 13 (65)
I0320 17:58:08.744426  2344 net.cpp:137] Memory required for data: 16600
I0320 17:58:08.744432  2344 layer_factory.hpp:77] Creating layer loss
I0320 17:58:08.744437  2344 net.cpp:84] Creating Layer loss
I0320 17:58:08.744441  2344 net.cpp:406] loss <- ip
I0320 17:58:08.744444  2344 net.cpp:406] loss <- label
I0320 17:58:08.744449  2344 net.cpp:380] loss -> loss
I0320 17:58:08.744457  2344 layer_factory.hpp:77] Creating layer loss
I0320 17:58:08.744580  2344 net.cpp:122] Setting up loss
I0320 17:58:08.744586  2344 net.cpp:129] Top shape: (1)
I0320 17:58:08.744590  2344 net.cpp:132]     with loss weight 1
I0320 17:58:08.744596  2344 net.cpp:137] Memory required for data: 16604
I0320 17:58:08.744598  2344 net.cpp:198] loss needs backward computation.
I0320 17:58:08.744602  2344 net.cpp:198] ip needs backward computation.
I0320 17:58:08.744606  2344 net.cpp:198] conv needs backward computation.
I0320 17:58:08.744608  2344 net.cpp:200] data does not need backward computation.
I0320 17:58:08.744611  2344 net.cpp:242] This network produces output loss
I0320 17:58:08.744617  2344 net.cpp:255] Network initialization done.
Running caffe.test.test_net
..W0320 17:58:09.747437  2344 net.hpp:41] DEPRECATED: ForwardPrefilled() will be removed in a future version. Use Forward().
..I0320 17:58:09.836779  2344 net.cpp:51] Initializing net from parameters: 
name: "testnet"
force_backward: true
state {
  phase: TRAIN
  level: 0
}
layer {
  name: "data"
  type: "DummyData"
  top: "data"
  top: "label"
  dummy_data_param {
    data_filler {
      type: "gaussian"
      std: 1
    }
    data_filler {
      type: "constant"
    }
    num: 5
    num: 5
    channels: 2
    channels: 1
    height: 3
    height: 1
    width: 4
    width: 1
  }
}
layer {
  name: "conv"
  type: "Convolution"
  bottom: "data"
  top: "conv"
  param {
    decay_mult: 1
  }
  param {
    decay_mult: 0
  }
  convolution_param {
    num_output: 11
    pad: 3
    kernel_size: 2
    weight_filler {
      type: "gaussian"
      std: 1
    }
    bias_filler {
      type: "constant"
      value: 2
    }
  }
}
layer {
  name: "ip"
  type: "InnerProduct"
  bottom: "conv"
  top: "ip"
  inner_product_param {
    num_output: 13
    weight_filler {
      type: "gaussian"
      std: 2.5
    }
    bias_filler {
      type: "constant"
      value: -3
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip"
  bottom: "label"
  top: "loss"
}
I0320 17:58:09.836823  2344 layer_factory.hpp:77] Creating layer data
I0320 17:58:09.836833  2344 net.cpp:84] Creating Layer data
I0320 17:58:09.836838  2344 net.cpp:380] data -> data
I0320 17:58:09.836844  2344 net.cpp:380] data -> label
I0320 17:58:09.836853  2344 net.cpp:122] Setting up data
I0320 17:58:09.836858  2344 net.cpp:129] Top shape: 5 2 3 4 (120)
I0320 17:58:09.836877  2344 net.cpp:129] Top shape: 5 1 1 1 (5)
I0320 17:58:09.836880  2344 net.cpp:137] Memory required for data: 500
I0320 17:58:09.836884  2344 layer_factory.hpp:77] Creating layer conv
I0320 17:58:09.836901  2344 net.cpp:84] Creating Layer conv
I0320 17:58:09.836905  2344 net.cpp:406] conv <- data
I0320 17:58:09.836922  2344 net.cpp:380] conv -> conv
I0320 17:58:09.837237  2344 net.cpp:122] Setting up conv
I0320 17:58:09.837244  2344 net.cpp:129] Top shape: 5 11 8 9 (3960)
I0320 17:58:09.837249  2344 net.cpp:137] Memory required for data: 16340
I0320 17:58:09.837255  2344 layer_factory.hpp:77] Creating layer ip
I0320 17:58:09.837260  2344 net.cpp:84] Creating Layer ip
I0320 17:58:09.837265  2344 net.cpp:406] ip <- conv
I0320 17:58:09.837268  2344 net.cpp:380] ip -> ip
I0320 17:58:09.837352  2344 net.cpp:122] Setting up ip
I0320 17:58:09.837357  2344 net.cpp:129] Top shape: 5 13 (65)
I0320 17:58:09.837359  2344 net.cpp:137] Memory required for data: 16600
I0320 17:58:09.837364  2344 layer_factory.hpp:77] Creating layer loss
I0320 17:58:09.837369  2344 net.cpp:84] Creating Layer loss
I0320 17:58:09.837373  2344 net.cpp:406] loss <- ip
I0320 17:58:09.837375  2344 net.cpp:406] loss <- label
I0320 17:58:09.837379  2344 net.cpp:380] loss -> loss
I0320 17:58:09.837385  2344 layer_factory.hpp:77] Creating layer loss
I0320 17:58:09.837513  2344 net.cpp:122] Setting up loss
I0320 17:58:09.837520  2344 net.cpp:129] Top shape: (1)
I0320 17:58:09.837523  2344 net.cpp:132]     with loss weight 1
I0320 17:58:09.837528  2344 net.cpp:137] Memory required for data: 16604
I0320 17:58:09.837532  2344 net.cpp:198] loss needs backward computation.
I0320 17:58:09.837535  2344 net.cpp:198] ip needs backward computation.
I0320 17:58:09.837538  2344 net.cpp:198] conv needs backward computation.
I0320 17:58:09.837541  2344 net.cpp:200] data does not need backward computation.
I0320 17:58:09.837545  2344 net.cpp:242] This network produces output loss
I0320 17:58:09.837549  2344 net.cpp:255] Network initialization done.
I0320 17:58:09.841357  2344 net.cpp:51] Initializing net from parameters: 
name: "testnet"
force_backward: true
state {
  phase: TRAIN
  level: 0
}
layer {
  name: "data"
  type: "DummyData"
  top: "data"
  top: "label"
  dummy_data_param {
    data_filler {
      type: "gaussian"
      std: 1
    }
    data_filler {
      type: "constant"
    }
    num: 5
    num: 5
    channels: 2
    channels: 1
    height: 3
    height: 1
    width: 4
    width: 1
  }
}
layer {
  name: "conv"
  type: "Convolution"
  bottom: "data"
  top: "conv"
  param {
    decay_mult: 1
  }
  param {
    decay_mult: 0
  }
  convolution_param {
    num_output: 11
    pad: 3
    kernel_size: 2
    weight_filler {
      type: "gaussian"
      std: 1
    }
    bias_filler {
      type: "constant"
      value: 2
    }
  }
}
layer {
  name: "ip"
  type: "InnerProduct"
  bottom: "conv"
  top: "ip"
  inner_product_param {
    num_output: 13
    weight_filler {
      type: "gaussian"
      std: 2.5
    }
    bias_filler {
      type: "constant"
      value: -3
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip"
  bottom: "label"
  top: "loss"
}
I0320 17:58:09.841394  2344 layer_factory.hpp:77] Creating layer data
I0320 17:58:09.841400  2344 net.cpp:84] Creating Layer data
I0320 17:58:09.841405  2344 net.cpp:380] data -> data
I0320 17:58:09.841413  2344 net.cpp:380] data -> label
I0320 17:58:09.841421  2344 net.cpp:122] Setting up data
I0320 17:58:09.841425  2344 net.cpp:129] Top shape: 5 2 3 4 (120)
I0320 17:58:09.841428  2344 net.cpp:129] Top shape: 5 1 1 1 (5)
I0320 17:58:09.841430  2344 net.cpp:137] Memory required for data: 500
I0320 17:58:09.841434  2344 layer_factory.hpp:77] Creating layer conv
I0320 17:58:09.841439  2344 net.cpp:84] Creating Layer conv
I0320 17:58:09.841441  2344 net.cpp:406] conv <- data
I0320 17:58:09.841444  2344 net.cpp:380] conv -> conv
I0320 17:58:09.842205  2344 net.cpp:122] Setting up conv
I0320 17:58:09.842214  2344 net.cpp:129] Top shape: 5 11 8 9 (3960)
I0320 17:58:09.842217  2344 net.cpp:137] Memory required for data: 16340
I0320 17:58:09.842223  2344 layer_factory.hpp:77] Creating layer ip
I0320 17:58:09.842231  2344 net.cpp:84] Creating Layer ip
I0320 17:58:09.842233  2344 net.cpp:406] ip <- conv
I0320 17:58:09.842237  2344 net.cpp:380] ip -> ip
I0320 17:58:09.842329  2344 net.cpp:122] Setting up ip
I0320 17:58:09.842334  2344 net.cpp:129] Top shape: 5 13 (65)
I0320 17:58:09.842337  2344 net.cpp:137] Memory required for data: 16600
I0320 17:58:09.842341  2344 layer_factory.hpp:77] Creating layer loss
I0320 17:58:09.842346  2344 net.cpp:84] Creating Layer loss
I0320 17:58:09.842350  2344 net.cpp:406] loss <- ip
I0320 17:58:09.842352  2344 net.cpp:406] loss <- label
I0320 17:58:09.842355  2344 net.cpp:380] loss -> loss
I0320 17:58:09.842360  2344 layer_factory.hpp:77] Creating layer loss
I0320 17:58:09.842516  2344 net.cpp:122] Setting up loss
I0320 17:58:09.842523  2344 net.cpp:129] Top shape: (1)
I0320 17:58:09.842525  2344 net.cpp:132]     with loss weight 1
I0320 17:58:09.842530  2344 net.cpp:137] Memory required for data: 16604
I0320 17:58:09.842533  2344 net.cpp:198] loss needs backward computation.
I0320 17:58:09.842536  2344 net.cpp:198] ip needs backward computation.
I0320 17:58:09.842538  2344 net.cpp:198] conv needs backward computation.
I0320 17:58:09.842541  2344 net.cpp:200] data does not need backward computation.
I0320 17:58:09.842543  2344 net.cpp:242] This network produces output loss
I0320 17:58:09.842547  2344 net.cpp:255] Network initialization done.
.
Done caffe.test.test_net
__________

Attempt to restart MATLAB? [y or n]>>'

after I enter y:

       Segmentation violation detected at Tue Mar 20 17:58:09 2018
------------------------------------------------------------------------

Configuration:
  Crash Decoding      : Disabled - No sandbox or build area path
  Crash Mode          : continue (default)
  Current Graphics Driver: Unknown software 
  Current Visual      : None
  Default Encoding    : UTF-8
  Deployed            : false
  GNU C Library       : 2.23 stable
  Host Name           : han-B250M-D3V
  MATLAB Architecture : glnxa64
  MATLAB Entitlement ID: 6257193
  MATLAB Root         : /usr/local/MATLAB/R2016b
  MATLAB Version      : 9.1.0.441655 (R2016b)
  OpenGL              : software
  Operating System    : Linux 4.13.0-37-generic #42~16.04.1-Ubuntu SMP Wed Mar 7 16:03:28 UTC 2018 x86_64
  Processor ID        : x86 Family 6 Model 158 Stepping 9, GenuineIntel
  Virtual Machine     : Java 1.7.0_60-b19 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
  Window System       : No active display

Fault Count: 1


Abnormal termination:
Segmentation violation

Register State (from fault):
  RAX = 006f0062006c006f  RBX = 006f0062006c006e
  RCX = 00007f21548b1460  RDX = 006f0062006c006e
  RSP = 00007f220bdf3fd0  RBP = 00007f220bdf4020
  RSI = 00007f220bdf4100  RDI = 00007f220bdf4020

   R8 = 006f7f8354f714ce   R9 = 0000000000000000
  R10 = 000000000000007b  R11 = 00007f222a6039c0
  R12 = 00007f220bdf4100  R13 = 00007f2154899060
  R14 = 00007f220bdf4270  R15 = 00007f220bdf4bf8

  RIP = 00007f222a603a5b  EFL = 0000000000010206

   CS = 0033   FS = 0000   GS = 0000

Stack Trace (from fault):
[  0] 0x00007f222a603a5b /usr/local/MATLAB/R2016b/bin/glnxa64/libboost_filesystem.so.1.56.0+00059995 _ZNK5boost10filesystem4path8filenameEv+00000155
[  1] 0x00007f222a604b36 /usr/local/MATLAB/R2016b/bin/glnxa64/libboost_filesystem.so.1.56.0+00064310 _ZNK5boost10filesystem4path9extensionEv+00000022
[  2] 0x00007f222a604c62 /usr/local/MATLAB/R2016b/bin/glnxa64/libboost_filesystem.so.1.56.0+00064610 _ZN5boost10filesystem4path17replace_extensionERKS1_+00000034
[  3] 0x00007f219737aad8 /home/han/caffe/matlab/+caffe/private/caffe_.mexa64+00727768
[  4] 0x00007f219737af30 /home/han/caffe/matlab/+caffe/private/caffe_.mexa64+00728880
[  5] 0x00007f2197314acf /home/han/caffe/matlab/+caffe/private/caffe_.mexa64+00309967
[  6] 0x00007f2197311e7f /home/han/caffe/matlab/+caffe/private/caffe_.mexa64+00298623 mexFunction+00000169
[  7] 0x00007f221ce50caa     /usr/local/MATLAB/R2016b/bin/glnxa64/libmex.so+00175274 mexRunMexFile+00000106
[  8] 0x00007f221ce491a3     /usr/local/MATLAB/R2016b/bin/glnxa64/libmex.so+00143779
[  9] 0x00007f221ce4a345     /usr/local/MATLAB/R2016b/bin/glnxa64/libmex.so+00148293
[ 10] 0x00007f221c1498a3 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_dispatcher.so+00768163 _ZN8Mfh_file16dispatch_fh_implEMS_FviPP11mxArray_tagiS2_EiS2_iS2_+00000947
[ 11] 0x00007f221c14a16e /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_dispatcher.so+00770414 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000030
[ 12] 0x00007f2218f84847 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+11675719
[ 13] 0x00007f2218f84aab /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+11676331
[ 14] 0x00007f2218fea411 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+12092433
[ 15] 0x00007f2218910930 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+04909360
[ 16] 0x00007f2218912c3c /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+04918332
[ 17] 0x00007f221890f410 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+04903952
[ 18] 0x00007f221890a855 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+04884565
[ 19] 0x00007f221890ab69 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+04885353
[ 20] 0x00007f221890f20d /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+04903437
[ 21] 0x00007f221890f2e2 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+04903650
[ 22] 0x00007f2218a06688 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+05916296
[ 23] 0x00007f2218a08b2f /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+05925679
[ 24] 0x00007f2218e8710e /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+10637582
[ 25] 0x00007f2218e4eeab /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+10407595
[ 26] 0x00007f2218e4efb3 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+10407859
[ 27] 0x00007f2218e510d9 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+10416345
[ 28] 0x00007f2218ec9bbe /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+10910654
[ 29] 0x00007f2218eca072 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_lxe.so+10911858
[ 30] 0x00007f221b869941 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwm_interpreter.so+02443585 _Z51inEvalCmdWithLocalReturnInDesiredWSAndPublishEventsRKSbIDsSt11char_traitsIDsESaIDsEEPibbP15inWorkSpace_tag+00000065
[ 31] 0x00007f221cbaafc1   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00696257 _ZNK3iqm18InternalEvalPlugin24inEvalCmdWithLocalReturnERKSbIDsSt11char_traitsIDsESaIDsEEP15inWorkSpace_tag+00000097
[ 32] 0x00007f221cbac9db   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00702939 _ZN3iqm18InternalEvalPlugin7executeEP15inWorkSpace_tagRN5boost10shared_ptrIN14cmddistributor17IIPCompletedEventEEE+00000123
[ 33] 0x00007f221c4206cd   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwmcr.so+00624333
[ 34] 0x00007f221cb9fa0a   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00649738
[ 35] 0x00007f221cb8beb2   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00569010
[ 36] 0x00007f221b3e705a /usr/local/MATLAB/R2016b/bin/glnxa64/libmwbridge.so+00159834
[ 37] 0x00007f221b3e7617 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwbridge.so+00161303
[ 38] 0x00007f221b3ee519 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwbridge.so+00189721
[ 39] 0x00007f221b3ee614 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwbridge.so+00189972
[ 40] 0x00007f221b3eefa9 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwbridge.so+00192425 _Z8mnParserv+00000617
[ 41] 0x00007f221c40b243   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwmcr.so+00537155
[ 42] 0x00007f221c40d1ce   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwmcr.so+00545230
[ 43] 0x00007f221c40d849   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwmcr.so+00546889 _ZN5boost6detail17task_shared_stateINS_3_bi6bind_tIvPFvRKNS_8functionIFvvEEEENS2_5list1INS2_5valueIS6_EEEEEEvE6do_runEv+00000025
[ 44] 0x00007f221c40c236   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwmcr.so+00541238
[ 45] 0x00007f221cbd3b49   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00863049
[ 46] 0x00007f221cbc051c   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00783644 _ZN5boost6detail8function21function_obj_invoker0ISt8functionIFNS_3anyEvEES4_E6invokeERNS1_15function_bufferE+00000028
[ 47] 0x00007f221cbc01fc   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00782844 _ZN3iqm18PackagedTaskPlugin7executeEP15inWorkSpace_tagRN5boost10shared_ptrIN14cmddistributor17IIPCompletedEventEEE+00000428
[ 48] 0x00007f221cb9fa0a   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00649738
[ 49] 0x00007f221cb8b690   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00566928
[ 50] 0x00007f221cb8e048   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwiqm.so+00577608
[ 51] 0x00007f222c7e040a /usr/local/MATLAB/R2016b/bin/glnxa64/libmwservices.so+02634762
[ 52] 0x00007f222c7e19af /usr/local/MATLAB/R2016b/bin/glnxa64/libmwservices.so+02640303
[ 53] 0x00007f222c7e20e6 /usr/local/MATLAB/R2016b/bin/glnxa64/libmwservices.so+02642150 _Z25svWS_ProcessPendingEventsiib+00000102
[ 54] 0x00007f221c40b8c6   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwmcr.so+00538822
[ 55] 0x00007f221c40bc42   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwmcr.so+00539714
[ 56] 0x00007f221c3f98d6   /usr/local/MATLAB/R2016b/bin/glnxa64/libmwmcr.so+00465110
[ 57] 0x00007f222b3f96ba              /lib/x86_64-linux-gnu/libpthread.so.0+00030394
[ 58] 0x00007f222b12f41d                    /lib/x86_64-linux-gnu/libc.so.6+01078301 clone+00000109
[ 59] 0x0000000000000000                                   <unknown-module>+00000000


This error was detected while a MEX-file was running. If the MEX-file
is not an official MathWorks function, please examine its source code
for errors. Please consult the External Interfaces Guide for information
on debugging MEX-files.

If this problem is reproducible, please submit a Service Request via:
    http://www.mathworks.com/support/contact_us/

A technical support engineer might contact you with further information.

Thank you for your help.** This crash report has been saved to disk as /home/han/matlab_crash_dump.2301-1 **


Warning: The following error was caught while executing 'caffe.Solver' class
destructor:
Error using caffe_
Usage: caffe_('delete_solver', hSolver)

Error in caffe.Solver/delete (line 40)
      caffe_('delete_solver', self.hSolver_self);

Error in caffe.Solver (line 17)
    function self = Solver(varargin)

Error in caffe.test.test_solver (line 22)
      self.solver = caffe.Solver(solver_file);

Error in caffe.run_tests (line 14)
  run(caffe.test.test_solver) ... 
> In caffe.Solver (line 17)
  In caffe.test.test_solver (line 22)
  In caffe.run_tests (line 14) 
Caught "std::exception" Exception message is:
FatalException
Caught MathWorks::System::FatalException
[Please exit and restart MATLAB]>>

entry restart:

Undefined function or variable 'restart'.'

So does anyone know how to solve this problem? Thank you very much!

Operating system:ubuntu16.04
Compiler:make mattest
CUDA version (if applicable):8.0
CUDNN version (if applicable):5.1
BLAS:
Python or MATLAB version (for pycaffe and matcaffe respectively):MATLAB2016b

[zsqciel]

i met the same problem, would you please share your solutions if you solve it?

[EswarSaiKrish]

i met the same problem, would you please share your solutions if you solve it?

[yuexingyu]

i met the same problem, would you please share your solutions if you solve it?

[techping]

i met the same problem, would you please share your solutions if you solve it?

[soulslicer]

i met the same problem, would you please share your solutions if you solve it?

no but seriously though, matcaffe is completely borked. i tried to make it work on multiple versions of Matlab and prior commits of Caffe but could never make it work. your only solution is to switch to python

[BruceW91]

I met the same problem, would you please share your solutions if you solve it?

I tried to change version of GCC&g++ from 5.4 to 4.9, but it did not work. And my MATLAB version is also R2016b, I wonder is it caused by the version incompatibility? Bacause in the official website, there is a statement

[MaxChanger]

I met the same problem, Operating system:ubuntu16.04, Compiler:make mattest, CUDA version (if applicable):8.0, CUDNN version (if applicable):5.1, there was the same problem as the first person, but I saw some blog achieve the test by Matlab R2016b, so I don't think it's directly related to the version. But I also don't know how to solve the problem.

[Jee-King]

I met the same problem.
That may be caused by the caffe version. This is my solutionit which might help others.
Download and Compile this version：
https://github.com/gy1874/caffe-rpnbf-cudnn5