Error when trying the imagenet example

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Yingyu Liang

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May 13, 2015, 10:31:56 AM5/13/15
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Hi,

I tried the imagenet example, and got an error msg saying that the mdb fails with Permission Denied. I put the imagenet lmdb data in /home/common/imagenet and changed the proto txt accordingly; also generated the image mean file. Has anyone got such error before? Any suggestions will be greatly appreciated! 

The related part of the output is (and all the output is also shown below):

I0513 10:22:08.295487  1273 layer_factory.hpp:74] Creating layer data
I0513 10:22:08.295840  1273 net.cpp:84] Creating Layer data
I0513 10:22:08.295883  1273 net.cpp:338] data -> data
I0513 10:22:08.295980  1273 net.cpp:338] data -> label
I0513 10:22:08.296025  1273 net.cpp:113] Setting up data
F0513 10:22:08.296128  1273 db.hpp:109] Check failed: mdb_status == 0 (13 vs. 0) Permission denied
*** Check failure stack trace: ***
    @     0x7f456cb72b7d  google::LogMessage::Fail()
    @     0x7f456cb74c7f  google::LogMessage::SendToLog()
    @     0x7f456cb7276c  google::LogMessage::Flush()
    @     0x7f456cb7551d  google::LogMessageFatal::~LogMessageFatal()
    @     0x7f456cf820ce  caffe::db::LMDB::Open()
    @     0x7f456cf316e4  caffe::DataLayer<>::DataLayerSetUp()
    @     0x7f456cf167e6  caffe::BaseDataLayer<>::LayerSetUp()
    @     0x7f456cf168e9  caffe::BasePrefetchingDataLayer<>::LayerSetUp()
    @     0x7f456cf5bb72  caffe::Net<>::Init()
    @     0x7f456cf5d632  caffe::Net<>::Net()
    @     0x7f456ceb95b0  caffe::Solver<>::InitTrainNet()
    @     0x7f456ceba6c3  caffe::Solver<>::Init()
    @     0x7f456ceba896  caffe::Solver<>::Solver()
    @           0x40c4a0  caffe::GetSolver<>()
    @           0x406471  train()
    @           0x404a11  main
    @     0x7f456c2a9ec5  (unknown)
    @           0x404fbd  (unknown)
Aborted (core dumped)

====================================All output==========================================
I0513 10:22:07.979399  1273 caffe.cpp:113] Use GPU with device ID 0
I0513 10:22:08.286501  1273 caffe.cpp:121] Starting Optimization
I0513 10:22:08.286767  1273 solver.cpp:32] Initializing solver from parameters: 
test_iter: 1000
test_interval: 1000
base_lr: 0.01
display: 20
max_iter: 450000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 100000
snapshot: 10000
snapshot_prefix: "models/bvlc_reference_caffenet/caffenet_train"
solver_mode: GPU
net: "models/bvlc_reference_caffenet/train_val.prototxt"
I0513 10:22:08.287188  1273 solver.cpp:70] Creating training net from net file: models/bvlc_reference_caffenet/train_val.prototxt
I0513 10:22:08.288521  1273 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0513 10:22:08.288591  1273 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0513 10:22:08.288839  1273 net.cpp:42] Initializing net from parameters: 
name: "CaffeNet"
state {
  phase: TRAIN
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
  }
  data_param {
    source: "/home/common/imagenet/ilsvrc12_train_lmdb"
    batch_size: 256
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    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: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_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: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
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: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
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
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
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: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "label"
  top: "loss"
}
I0513 10:22:08.295487  1273 layer_factory.hpp:74] Creating layer data
I0513 10:22:08.295840  1273 net.cpp:84] Creating Layer data
I0513 10:22:08.295883  1273 net.cpp:338] data -> data
I0513 10:22:08.295980  1273 net.cpp:338] data -> label
I0513 10:22:08.296025  1273 net.cpp:113] Setting up data
F0513 10:22:08.296128  1273 db.hpp:109] Check failed: mdb_status == 0 (13 vs. 0) Permission denied
*** Check failure stack trace: ***
    @     0x7f456cb72b7d  google::LogMessage::Fail()
    @     0x7f456cb74c7f  google::LogMessage::SendToLog()
    @     0x7f456cb7276c  google::LogMessage::Flush()
    @     0x7f456cb7551d  google::LogMessageFatal::~LogMessageFatal()
    @     0x7f456cf820ce  caffe::db::LMDB::Open()
    @     0x7f456cf316e4  caffe::DataLayer<>::DataLayerSetUp()
    @     0x7f456cf167e6  caffe::BaseDataLayer<>::LayerSetUp()
    @     0x7f456cf168e9  caffe::BasePrefetchingDataLayer<>::LayerSetUp()
    @     0x7f456cf5bb72  caffe::Net<>::Init()
    @     0x7f456cf5d632  caffe::Net<>::Net()
    @     0x7f456ceb95b0  caffe::Solver<>::InitTrainNet()
    @     0x7f456ceba6c3  caffe::Solver<>::Init()
    @     0x7f456ceba896  caffe::Solver<>::Solver()
    @           0x40c4a0  caffe::GetSolver<>()
    @           0x406471  train()
    @           0x404a11  main
    @     0x7f456c2a9ec5  (unknown)
    @           0x404fbd  (unknown)
Aborted (core dumped)
 

Alykhan Tejani

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May 13, 2015, 10:39:50 AM5/13/15
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I think the lmdb path has to be relative from where you run the script. i.e. not the absolute path

Yingyu Liang

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May 13, 2015, 10:48:19 AM5/13/15
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Thanks for the reply! I also tried the relative path (I run the script in the folder /home/theory/caffe/):

source: "../../common/imagenet/ilsvrc12_train_lmdb"

But I still get the same error. Any other thoughts?

zho...@udel.edu

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Jul 16, 2015, 7:59:44 PM7/16/15
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I had the same problem as you did yesterday. I figured it out by changing the access permission to the data set. It worked. You can try change the permission to your entire imagenet folder. 

Hope this helps.

Yin

Xing Di

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Oct 20, 2015, 7:30:54 PM10/20/15
to Caffe Users
same problem !

Jan C Peters

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Oct 21, 2015, 9:33:06 AM10/21/15
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Yeah that is not a Caffe problem at all. Your (Linux) user just hasn't the required access rights to the folder the db is in. Change them with chmod if you have the necessary privileges or ask the responsible IT guy. And I am very certain that absolute paths do work.

Jan C Peters

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Oct 21, 2015, 9:37:07 AM10/21/15
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The reason that you don't have access is probably because no (regular) user has access to another user's home folder by default.

husi...@gmail.com

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Mar 26, 2017, 3:13:20 PM3/26/17
to Caffe Users
I am experiencing the same problem, could you please describe how you solved this problem?I tried Jan's suggestion and it does not work
Thanks a lot

在 2015年5月13日星期三 UTC-4上午10:31:56，Yingyu Liang写道：

husi...@gmail.com

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Mar 26, 2017, 3:21:55 PM3/26/17
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got it now, just need to add sudo before the execute the commend
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