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rbgirshick / py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
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Faster rcnn zf trainSolving...python quits unexpectedly error #694

Open andyu78945 opened 7 years ago

andyu78945 commented 7 years ago

Hi, When I'm training my own data in faster rcnn, I go into the solving step, python will quits unexpectedly.I don't know what happen.

Liude-MacBook-Pro:~ andyu78945$ cd /Users/andyu78945/py-faster-rcnn/
Liude-MacBook-Pro:py-faster-rcnn andyu78945$ ./experiments/scripts/faster_rcnn_alt_opt.sh 0 ZF pascal_voc
+ set -e
+ export PYTHONUNBUFFERED=True
+ PYTHONUNBUFFERED=True
+ GPU_ID=0
+ NET=ZF
+ NET_lc=ZF
+ DATASET=pascal_voc
+ array=($@)
+ len=3
+ EXTRA_ARGS=
+ EXTRA_ARGS_SLUG=
+ case $DATASET in
+ TRAIN_IMDB=voc_2007_trainval
+ TEST_IMDB=voc_2007_test
+ PT_DIR=pascal_voc
+ ITERS=10000
++ date +%Y-%m-%d_%H-%M-%S
+ LOG=experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-09-26_21-27-24
+ exec
++ tee -a experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-09-26_21-27-24
+ echo Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-09-26_21-27-24
Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-09-26_21-27-24
+ ./tools/train_faster_rcnn_alt_opt.py --net_name ZF --weights data/imagenet_models/ZF.v2.caffemodel --imdb voc_2007_trainval --cfg experiments/cfgs/faster_rcnn_alt_opt.yml
Called with args:
Namespace(cfg_file='experiments/cfgs/faster_rcnn_alt_opt.yml', imdb_name='voc_2007_trainval', net_name='ZF', pretrained_model='data/imagenet_models/ZF.v2.caffemodel', set_cfgs=None)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Stage 1 RPN, init from ImageNet model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Init model: data/imagenet_models/ZF.v2.caffemodel
Using config:
{'DATA_DIR': '/Users/andyu78945/py-faster-rcnn/data',
 'DEDUP_BOXES': 0.0625,
 'EPS': 1e-14,
 'EXP_DIR': 'faster_rcnn_alt_opt',
 'GPU_ID': 0,
 'MATLAB': 'matlab',
 'MODELS_DIR': '/Users/andyu78945/py-faster-rcnn/models/pascal_voc',
 'PIXEL_MEANS': array([[[ 102.9801,  115.9465,  122.7717]]]),
 'RNG_SEED': 3,
 'ROOT_DIR': '/Users/andyu78945/py-faster-rcnn',
 'TEST': {'BBOX_REG': True,
          'HAS_RPN': True,
          'MAX_SIZE': 256,
          'NMS': 0.3,
          'PROPOSAL_METHOD': 'selective_search',
          'RPN_MIN_SIZE': 16,
          'RPN_NMS_THRESH': 0.7,
          'RPN_POST_NMS_TOP_N': 300,
          'RPN_PRE_NMS_TOP_N': 6000,
          'SCALES': [224],
          'SVM': False},
 'TRAIN': {'ASPECT_GROUPING': True,
           'BATCH_SIZE': 32,
           'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
           'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0],
           'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2],
           'BBOX_NORMALIZE_TARGETS': True,
           'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': False,
           'BBOX_REG': False,
           'BBOX_THRESH': 0.5,
           'BG_THRESH_HI': 0.5,
           'BG_THRESH_LO': 0.0,
           'FG_FRACTION': 0.25,
           'FG_THRESH': 0.5,
           'HAS_RPN': True,
           'IMS_PER_BATCH': 1,
           'MAX_SIZE': 224,
           'PROPOSAL_METHOD': 'gt',
           'RPN_BATCHSIZE': 256,
           'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
           'RPN_CLOBBER_POSITIVES': False,
           'RPN_FG_FRACTION': 0.5,
           'RPN_MIN_SIZE': 16,
           'RPN_NEGATIVE_OVERLAP': 0.3,
           'RPN_NMS_THRESH': 0.7,
           'RPN_POSITIVE_OVERLAP': 0.7,
           'RPN_POSITIVE_WEIGHT': -1.0,
           'RPN_POST_NMS_TOP_N': 2000,
           'RPN_PRE_NMS_TOP_N': 12000,
           'SCALES': [224],
           'SNAPSHOT_INFIX': 'stage1',
           'SNAPSHOT_ITERS': 30000,
           'USE_FLIPPED': True,
           'USE_PREFETCH': False},
 'USE_GPU_NMS': False}
Loaded dataset `voc_2007_trainval` for training
Set proposal method: gt
Appending horizontally-flipped training examples...
wrote gt roidb to /Users/andyu78945/py-faster-rcnn/data/cache/voc_2007_trainval_gt_roidb.pkl
done
Preparing training data...
done
roidb len: 160
Output will be saved to `/Users/andyu78945/py-faster-rcnn/output/faster_rcnn_alt_opt/voc_2007_trainval`
Filtered 0 roidb entries: 160 -> 160
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0926 21:27:25.822978 3110372288 solver.cpp:48] Initializing solver from parameters: 
train_net: "models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt"
base_lr: 0.001
display: 20
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 10000
snapshot: 0
snapshot_prefix: "zf_rpn"
average_loss: 100
I0926 21:27:25.823182 3110372288 solver.cpp:81] Creating training net from train_net file: models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt
I0926 21:27:25.823637 3110372288 net.cpp:49] Initializing net from parameters: 
name: "ZF"
state {
  phase: TRAIN
}
layer {
  name: "input-data"
  type: "Python"
  top: "data"
  top: "im_info"
  top: "gt_boxes"
  python_param {
    module: "roi_data_layer.layer"
    layer: "RoIDataLayer"
    param_str: "\'num_classes\': 6"
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 96
    pad: 3
    kernel_size: 7
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
    engine: CAFFE
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 1
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    stride: 2
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
    engine: CAFFE
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 1
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "rpn_conv1"
  type: "Convolution"
  bottom: "conv5"
  top: "rpn_conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "rpn_relu1"
  type: "ReLU"
  bottom: "rpn_conv1"
  top: "rpn_conv1"
}
layer {
  name: "rpn_cls_score"
  type: "Convolution"
  bottom: "rpn_conv1"
  top: "rpn_cls_score"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 18
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "rpn_bbox_pred"
  type: "Convolution"
  bottom: "rpn_conv1"
  top: "rpn_bbox_pred"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 36
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "rpn_cls_score_reshape"
  type: "Reshape"
  bottom: "rpn_cls_score"
  top: "rpn_cls_score_reshape"
  reshape_param {
    shape {
      dim: 0
      dim: 2
      dim: -1
      dim: 0
    }
  }
}
layer {
  name: "rpn-data"
  type: "Python"
  bottom: "rpn_cls_score"
  bottom: "gt_boxes"
  bottom: "im_info"
  bottom: "data"
  top: "rpn_labels"
  top: "rpn_bbox_targets"
  top: "rpn_bbox_inside_weights"
  top: "rpn_bbox_outside_weights"
  python_param {
    module: "rpn.anchor_target_layer"
    layer: "AnchorTargetLayer"
    param_str: "\'feat_stride\': 16"
  }
}
layer {
  name: "rpn_loss_cls"
  type: "SoftmaxWithLoss"
  bottom: "rpn_cls_score_reshape"
  bottom: "rpn_labels"
  top: "rpn_cls_loss"
  loss_weight: 1
  propagate_down: true
  propagate_down: false
  loss_param {
    ignore_label: -1
    normalize: true
  }
}
layer {
  name: "rpn_loss_bbox"
  type: "SmoothL1Loss"
  bottom: "rpn_bbox_pred"
  bottom: "rpn_bbox_targets"
  bottom: "rpn_bbox_inside_weights"
  bottom: "rpn_bbox_outside_weights"
  top: "rpn_loss_bbox"
  loss_weight: 1
  smooth_l1_loss_param {
    sigma: 3
  }
}
layer {
  name: "dummy_roi_pool_conv5"
  type: "DummyData"
  top: "dummy_roi_pool_conv5"
  dummy_data_param {
    data_filler {
      type: "gaussian"
      std: 0.01
    }
    shape {
      dim: 1
      dim: 9216
    }
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "dummy_roi_pool_conv5"
  top: "fc6"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "silence_fc7"
  type: "Silence"
  bottom: "fc7"
}
I0926 21:27:25.823921 3110372288 layer_factory.hpp:77] Creating layer input-data
I0926 21:27:25.825009 3110372288 net.cpp:106] Creating Layer input-data
I0926 21:27:25.825080 3110372288 net.cpp:411] input-data -> data
I0926 21:27:25.825131 3110372288 net.cpp:411] input-data -> im_info
I0926 21:27:25.825165 3110372288 net.cpp:411] input-data -> gt_boxes
RoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1}
I0926 21:27:25.826084 3110372288 net.cpp:150] Setting up input-data
I0926 21:27:25.826138 3110372288 net.cpp:157] Top shape: 1 3 224 224 (150528)
I0926 21:27:25.826182 3110372288 net.cpp:157] Top shape: 1 3 (3)
I0926 21:27:25.826223 3110372288 net.cpp:157] Top shape: 1 4 (4)
I0926 21:27:25.826261 3110372288 net.cpp:165] Memory required for data: 602140
I0926 21:27:25.826298 3110372288 layer_factory.hpp:77] Creating layer data_input-data_0_split
I0926 21:27:25.826341 3110372288 net.cpp:106] Creating Layer data_input-data_0_split
I0926 21:27:25.826377 3110372288 net.cpp:454] data_input-data_0_split <- data
I0926 21:27:25.826411 3110372288 net.cpp:411] data_input-data_0_split -> data_input-data_0_split_0
I0926 21:27:25.826449 3110372288 net.cpp:411] data_input-data_0_split -> data_input-data_0_split_1
I0926 21:27:25.826495 3110372288 net.cpp:150] Setting up data_input-data_0_split
I0926 21:27:25.826532 3110372288 net.cpp:157] Top shape: 1 3 224 224 (150528)
I0926 21:27:25.826565 3110372288 net.cpp:157] Top shape: 1 3 224 224 (150528)
I0926 21:27:25.826601 3110372288 net.cpp:165] Memory required for data: 1806364
I0926 21:27:25.826634 3110372288 layer_factory.hpp:77] Creating layer conv1
I0926 21:27:25.826674 3110372288 net.cpp:106] Creating Layer conv1
I0926 21:27:25.826709 3110372288 net.cpp:454] conv1 <- data_input-data_0_split_0
I0926 21:27:25.826745 3110372288 net.cpp:411] conv1 -> conv1
I0926 21:27:25.826910 3110372288 net.cpp:150] Setting up conv1
I0926 21:27:25.826939 3110372288 net.cpp:157] Top shape: 1 96 112 112 (1204224)
I0926 21:27:25.826968 3110372288 net.cpp:165] Memory required for data: 6623260
I0926 21:27:25.827008 3110372288 layer_factory.hpp:77] Creating layer relu1
I0926 21:27:25.827049 3110372288 net.cpp:106] Creating Layer relu1
I0926 21:27:25.827085 3110372288 net.cpp:454] relu1 <- conv1
I0926 21:27:25.827121 3110372288 net.cpp:397] relu1 -> conv1 (in-place)
I0926 21:27:25.827184 3110372288 net.cpp:150] Setting up relu1
I0926 21:27:25.827236 3110372288 net.cpp:157] Top shape: 1 96 112 112 (1204224)
I0926 21:27:25.827329 3110372288 net.cpp:165] Memory required for data: 11440156
I0926 21:27:25.827364 3110372288 layer_factory.hpp:77] Creating layer norm1
I0926 21:27:25.827401 3110372288 net.cpp:106] Creating Layer norm1
I0926 21:27:25.827545 3110372288 net.cpp:454] norm1 <- conv1
I0926 21:27:25.827579 3110372288 net.cpp:411] norm1 -> norm1
I0926 21:27:25.827627 3110372288 net.cpp:150] Setting up norm1
I0926 21:27:25.827652 3110372288 net.cpp:157] Top shape: 1 96 112 112 (1204224)
I0926 21:27:25.827684 3110372288 net.cpp:165] Memory required for data: 16257052
I0926 21:27:25.827718 3110372288 layer_factory.hpp:77] Creating layer pool1
I0926 21:27:25.827764 3110372288 net.cpp:106] Creating Layer pool1
I0926 21:27:25.827800 3110372288 net.cpp:454] pool1 <- norm1
I0926 21:27:25.827826 3110372288 net.cpp:411] pool1 -> pool1
I0926 21:27:25.827858 3110372288 net.cpp:150] Setting up pool1
I0926 21:27:25.827888 3110372288 net.cpp:157] Top shape: 1 96 57 57 (311904)
I0926 21:27:25.827915 3110372288 net.cpp:165] Memory required for data: 17504668
I0926 21:27:25.827971 3110372288 layer_factory.hpp:77] Creating layer conv2
I0926 21:27:25.828006 3110372288 net.cpp:106] Creating Layer conv2
I0926 21:27:25.828054 3110372288 net.cpp:454] conv2 <- pool1
I0926 21:27:25.828186 3110372288 net.cpp:411] conv2 -> conv2
I0926 21:27:25.829418 3110372288 net.cpp:150] Setting up conv2
I0926 21:27:25.829468 3110372288 net.cpp:157] Top shape: 1 256 29 29 (215296)
I0926 21:27:25.829493 3110372288 net.cpp:165] Memory required for data: 18365852
I0926 21:27:25.829516 3110372288 layer_factory.hpp:77] Creating layer relu2
I0926 21:27:25.829540 3110372288 net.cpp:106] Creating Layer relu2
I0926 21:27:25.829562 3110372288 net.cpp:454] relu2 <- conv2
I0926 21:27:25.829622 3110372288 net.cpp:397] relu2 -> conv2 (in-place)
I0926 21:27:25.829655 3110372288 net.cpp:150] Setting up relu2
I0926 21:27:25.829685 3110372288 net.cpp:157] Top shape: 1 256 29 29 (215296)
I0926 21:27:25.829733 3110372288 net.cpp:165] Memory required for data: 19227036
I0926 21:27:25.829774 3110372288 layer_factory.hpp:77] Creating layer norm2
I0926 21:27:25.829823 3110372288 net.cpp:106] Creating Layer norm2
I0926 21:27:25.829850 3110372288 net.cpp:454] norm2 <- conv2
I0926 21:27:25.829901 3110372288 net.cpp:411] norm2 -> norm2
I0926 21:27:25.829960 3110372288 net.cpp:150] Setting up norm2
I0926 21:27:25.829988 3110372288 net.cpp:157] Top shape: 1 256 29 29 (215296)
I0926 21:27:25.830015 3110372288 net.cpp:165] Memory required for data: 20088220
I0926 21:27:25.830056 3110372288 layer_factory.hpp:77] Creating layer pool2
I0926 21:27:25.830091 3110372288 net.cpp:106] Creating Layer pool2
I0926 21:27:25.830140 3110372288 net.cpp:454] pool2 <- norm2
I0926 21:27:25.830169 3110372288 net.cpp:411] pool2 -> pool2
I0926 21:27:25.830217 3110372288 net.cpp:150] Setting up pool2
I0926 21:27:25.830262 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)
I0926 21:27:25.830292 3110372288 net.cpp:165] Memory required for data: 20318620
I0926 21:27:25.830330 3110372288 layer_factory.hpp:77] Creating layer conv3
I0926 21:27:25.830381 3110372288 net.cpp:106] Creating Layer conv3
I0926 21:27:25.830430 3110372288 net.cpp:454] conv3 <- pool2
I0926 21:27:25.830461 3110372288 net.cpp:411] conv3 -> conv3
I0926 21:27:25.832145 3110372288 net.cpp:150] Setting up conv3
I0926 21:27:25.832204 3110372288 net.cpp:157] Top shape: 1 384 15 15 (86400)
I0926 21:27:25.832227 3110372288 net.cpp:165] Memory required for data: 20664220
I0926 21:27:25.832252 3110372288 layer_factory.hpp:77] Creating layer relu3
I0926 21:27:25.832278 3110372288 net.cpp:106] Creating Layer relu3
I0926 21:27:25.832298 3110372288 net.cpp:454] relu3 <- conv3
I0926 21:27:25.832320 3110372288 net.cpp:397] relu3 -> conv3 (in-place)
I0926 21:27:25.832383 3110372288 net.cpp:150] Setting up relu3
I0926 21:27:25.832412 3110372288 net.cpp:157] Top shape: 1 384 15 15 (86400)
I0926 21:27:25.832443 3110372288 net.cpp:165] Memory required for data: 21009820
I0926 21:27:25.832499 3110372288 layer_factory.hpp:77] Creating layer conv4
I0926 21:27:25.832537 3110372288 net.cpp:106] Creating Layer conv4
I0926 21:27:25.832572 3110372288 net.cpp:454] conv4 <- conv3
I0926 21:27:25.832600 3110372288 net.cpp:411] conv4 -> conv4
I0926 21:27:25.835286 3110372288 net.cpp:150] Setting up conv4
I0926 21:27:25.835361 3110372288 net.cpp:157] Top shape: 1 384 15 15 (86400)
I0926 21:27:25.835384 3110372288 net.cpp:165] Memory required for data: 21355420
I0926 21:27:25.835407 3110372288 layer_factory.hpp:77] Creating layer relu4
I0926 21:27:25.835436 3110372288 net.cpp:106] Creating Layer relu4
I0926 21:27:25.835466 3110372288 net.cpp:454] relu4 <- conv4
I0926 21:27:25.835495 3110372288 net.cpp:397] relu4 -> conv4 (in-place)
I0926 21:27:25.835573 3110372288 net.cpp:150] Setting up relu4
I0926 21:27:25.835609 3110372288 net.cpp:157] Top shape: 1 384 15 15 (86400)
I0926 21:27:25.835638 3110372288 net.cpp:165] Memory required for data: 21701020
I0926 21:27:25.835666 3110372288 layer_factory.hpp:77] Creating layer conv5
I0926 21:27:25.835716 3110372288 net.cpp:106] Creating Layer conv5
I0926 21:27:25.835750 3110372288 net.cpp:454] conv5 <- conv4
I0926 21:27:25.835794 3110372288 net.cpp:411] conv5 -> conv5
I0926 21:27:25.837648 3110372288 net.cpp:150] Setting up conv5
I0926 21:27:25.837716 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)
I0926 21:27:25.837740 3110372288 net.cpp:165] Memory required for data: 21931420
I0926 21:27:25.837766 3110372288 layer_factory.hpp:77] Creating layer relu5
I0926 21:27:25.837792 3110372288 net.cpp:106] Creating Layer relu5
I0926 21:27:25.837813 3110372288 net.cpp:454] relu5 <- conv5
I0926 21:27:25.837836 3110372288 net.cpp:397] relu5 -> conv5 (in-place)
I0926 21:27:25.837860 3110372288 net.cpp:150] Setting up relu5
I0926 21:27:25.837887 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)
I0926 21:27:25.837913 3110372288 net.cpp:165] Memory required for data: 22161820
I0926 21:27:25.837939 3110372288 layer_factory.hpp:77] Creating layer rpn_conv1
I0926 21:27:25.837987 3110372288 net.cpp:106] Creating Layer rpn_conv1
I0926 21:27:25.838016 3110372288 net.cpp:454] rpn_conv1 <- conv5
I0926 21:27:25.838050 3110372288 net.cpp:411] rpn_conv1 -> rpn_conv1
I0926 21:27:25.844331 3110372288 net.cpp:150] Setting up rpn_conv1
I0926 21:27:25.844410 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)
I0926 21:27:25.844435 3110372288 net.cpp:165] Memory required for data: 22392220
I0926 21:27:25.844460 3110372288 layer_factory.hpp:77] Creating layer rpn_relu1
I0926 21:27:25.844485 3110372288 net.cpp:106] Creating Layer rpn_relu1
I0926 21:27:25.844506 3110372288 net.cpp:454] rpn_relu1 <- rpn_conv1
I0926 21:27:25.844528 3110372288 net.cpp:397] rpn_relu1 -> rpn_conv1 (in-place)
I0926 21:27:25.844591 3110372288 net.cpp:150] Setting up rpn_relu1
I0926 21:27:25.844637 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)
I0926 21:27:25.844676 3110372288 net.cpp:165] Memory required for data: 22622620
I0926 21:27:25.844707 3110372288 layer_factory.hpp:77] Creating layer rpn_conv1_rpn_relu1_0_split
I0926 21:27:25.844739 3110372288 net.cpp:106] Creating Layer rpn_conv1_rpn_relu1_0_split
I0926 21:27:25.844765 3110372288 net.cpp:454] rpn_conv1_rpn_relu1_0_split <- rpn_conv1
I0926 21:27:25.844797 3110372288 net.cpp:411] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_0
I0926 21:27:25.844842 3110372288 net.cpp:411] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_1
I0926 21:27:25.844883 3110372288 net.cpp:150] Setting up rpn_conv1_rpn_relu1_0_split
I0926 21:27:25.844962 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)
I0926 21:27:25.845007 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)
I0926 21:27:25.845037 3110372288 net.cpp:165] Memory required for data: 23083420
I0926 21:27:25.845065 3110372288 layer_factory.hpp:77] Creating layer rpn_cls_score
I0926 21:27:25.845113 3110372288 net.cpp:106] Creating Layer rpn_cls_score
I0926 21:27:25.845145 3110372288 net.cpp:454] rpn_cls_score <- rpn_conv1_rpn_relu1_0_split_0
I0926 21:27:25.845185 3110372288 net.cpp:411] rpn_cls_score -> rpn_cls_score
I0926 21:27:25.845288 3110372288 net.cpp:150] Setting up rpn_cls_score
I0926 21:27:25.845337 3110372288 net.cpp:157] Top shape: 1 18 15 15 (4050)
I0926 21:27:25.845377 3110372288 net.cpp:165] Memory required for data: 23099620
I0926 21:27:25.845409 3110372288 layer_factory.hpp:77] Creating layer rpn_cls_score_rpn_cls_score_0_split
I0926 21:27:25.845443 3110372288 net.cpp:106] Creating Layer rpn_cls_score_rpn_cls_score_0_split
I0926 21:27:25.845492 3110372288 net.cpp:454] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score
I0926 21:27:25.845526 3110372288 net.cpp:411] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0
I0926 21:27:25.845583 3110372288 net.cpp:411] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1
I0926 21:27:25.845623 3110372288 net.cpp:150] Setting up rpn_cls_score_rpn_cls_score_0_split
I0926 21:27:25.845716 3110372288 net.cpp:157] Top shape: 1 18 15 15 (4050)
I0926 21:27:25.845747 3110372288 net.cpp:157] Top shape: 1 18 15 15 (4050)
I0926 21:27:25.845777 3110372288 net.cpp:165] Memory required for data: 23132020
I0926 21:27:25.845806 3110372288 layer_factory.hpp:77] Creating layer rpn_bbox_pred
I0926 21:27:25.845865 3110372288 net.cpp:106] Creating Layer rpn_bbox_pred
I0926 21:27:25.845897 3110372288 net.cpp:454] rpn_bbox_pred <- rpn_conv1_rpn_relu1_0_split_1
I0926 21:27:25.845929 3110372288 net.cpp:411] rpn_bbox_pred -> rpn_bbox_pred
I0926 21:27:25.846082 3110372288 net.cpp:150] Setting up rpn_bbox_pred
I0926 21:27:25.846112 3110372288 net.cpp:157] Top shape: 1 36 15 15 (8100)
I0926 21:27:25.846134 3110372288 net.cpp:165] Memory required for data: 23164420
I0926 21:27:25.846156 3110372288 layer_factory.hpp:77] Creating layer rpn_cls_score_reshape
I0926 21:27:25.846249 3110372288 net.cpp:106] Creating Layer rpn_cls_score_reshape
I0926 21:27:25.846290 3110372288 net.cpp:454] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0
I0926 21:27:25.846326 3110372288 net.cpp:411] rpn_cls_score_reshape -> rpn_cls_score_reshape
I0926 21:27:25.846385 3110372288 net.cpp:150] Setting up rpn_cls_score_reshape
I0926 21:27:25.846417 3110372288 net.cpp:157] Top shape: 1 2 135 15 (4050)
I0926 21:27:25.846446 3110372288 net.cpp:165] Memory required for data: 23180620
I0926 21:27:25.846472 3110372288 layer_factory.hpp:77] Creating layer rpn-data
I0926 21:27:25.846982 3110372288 net.cpp:106] Creating Layer rpn-data
I0926 21:27:25.847013 3110372288 net.cpp:454] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1
I0926 21:27:25.847035 3110372288 net.cpp:454] rpn-data <- gt_boxes
I0926 21:27:25.847056 3110372288 net.cpp:454] rpn-data <- im_info
I0926 21:27:25.847149 3110372288 net.cpp:454] rpn-data <- data_input-data_0_split_1
I0926 21:27:25.847187 3110372288 net.cpp:411] rpn-data -> rpn_labels
I0926 21:27:25.847240 3110372288 net.cpp:411] rpn-data -> rpn_bbox_targets
I0926 21:27:25.847286 3110372288 net.cpp:411] rpn-data -> rpn_bbox_inside_weights
I0926 21:27:25.847323 3110372288 net.cpp:411] rpn-data -> rpn_bbox_outside_weights
I0926 21:27:25.848160 3110372288 net.cpp:150] Setting up rpn-data
I0926 21:27:25.848197 3110372288 net.cpp:157] Top shape: 1 1 135 15 (2025)
I0926 21:27:25.848222 3110372288 net.cpp:157] Top shape: 1 36 15 15 (8100)
I0926 21:27:25.848254 3110372288 net.cpp:157] Top shape: 1 36 15 15 (8100)
I0926 21:27:25.848307 3110372288 net.cpp:157] Top shape: 1 36 15 15 (8100)
I0926 21:27:25.848340 3110372288 net.cpp:165] Memory required for data: 23285920
I0926 21:27:25.848368 3110372288 layer_factory.hpp:77] Creating layer rpn_loss_cls
I0926 21:27:25.848398 3110372288 net.cpp:106] Creating Layer rpn_loss_cls
I0926 21:27:25.848424 3110372288 net.cpp:454] rpn_loss_cls <- rpn_cls_score_reshape
I0926 21:27:25.848451 3110372288 net.cpp:454] rpn_loss_cls <- rpn_labels
I0926 21:27:25.848479 3110372288 net.cpp:411] rpn_loss_cls -> rpn_cls_loss
I0926 21:27:25.848527 3110372288 layer_factory.hpp:77] Creating layer rpn_loss_cls
I0926 21:27:25.848604 3110372288 net.cpp:150] Setting up rpn_loss_cls
I0926 21:27:25.848644 3110372288 net.cpp:157] Top shape: (1)
I0926 21:27:25.848688 3110372288 net.cpp:160]     with loss weight 1
I0926 21:27:25.848745 3110372288 net.cpp:165] Memory required for data: 23285924
I0926 21:27:25.848793 3110372288 layer_factory.hpp:77] Creating layer rpn_loss_bbox
I0926 21:27:25.848842 3110372288 net.cpp:106] Creating Layer rpn_loss_bbox
I0926 21:27:25.848906 3110372288 net.cpp:454] rpn_loss_bbox <- rpn_bbox_pred
I0926 21:27:25.848963 3110372288 net.cpp:454] rpn_loss_bbox <- rpn_bbox_targets
I0926 21:27:25.849027 3110372288 net.cpp:454] rpn_loss_bbox <- rpn_bbox_inside_weights
I0926 21:27:25.849084 3110372288 net.cpp:454] rpn_loss_bbox <- rpn_bbox_outside_weights
I0926 21:27:25.849131 3110372288 net.cpp:411] rpn_loss_bbox -> rpn_loss_bbox
I0926 21:27:25.849236 3110372288 net.cpp:150] Setting up rpn_loss_bbox
I0926 21:27:25.849272 3110372288 net.cpp:157] Top shape: (1)
I0926 21:27:25.849308 3110372288 net.cpp:160]     with loss weight 1
I0926 21:27:25.849351 3110372288 net.cpp:165] Memory required for data: 23285928
I0926 21:27:25.849457 3110372288 layer_factory.hpp:77] Creating layer dummy_roi_pool_conv5
I0926 21:27:25.849503 3110372288 net.cpp:106] Creating Layer dummy_roi_pool_conv5
I0926 21:27:25.849531 3110372288 net.cpp:411] dummy_roi_pool_conv5 -> dummy_roi_pool_conv5
I0926 21:27:25.849573 3110372288 net.cpp:150] Setting up dummy_roi_pool_conv5
I0926 21:27:25.849656 3110372288 net.cpp:157] Top shape: 1 9216 (9216)
I0926 21:27:25.849700 3110372288 net.cpp:165] Memory required for data: 23322792
I0926 21:27:25.849730 3110372288 layer_factory.hpp:77] Creating layer fc6
I0926 21:27:25.849768 3110372288 net.cpp:106] Creating Layer fc6
I0926 21:27:25.849862 3110372288 net.cpp:454] fc6 <- dummy_roi_pool_conv5
I0926 21:27:25.849958 3110372288 net.cpp:411] fc6 -> fc6
I0926 21:27:25.929716 3110372288 net.cpp:150] Setting up fc6
I0926 21:27:25.929802 3110372288 net.cpp:157] Top shape: 1 4096 (4096)
I0926 21:27:25.929832 3110372288 net.cpp:165] Memory required for data: 23339176
I0926 21:27:25.929874 3110372288 layer_factory.hpp:77] Creating layer relu6
I0926 21:27:25.929929 3110372288 net.cpp:106] Creating Layer relu6
I0926 21:27:25.930035 3110372288 net.cpp:454] relu6 <- fc6
I0926 21:27:25.930088 3110372288 net.cpp:397] relu6 -> fc6 (in-place)
I0926 21:27:25.930126 3110372288 net.cpp:150] Setting up relu6
I0926 21:27:25.930156 3110372288 net.cpp:157] Top shape: 1 4096 (4096)
I0926 21:27:25.930182 3110372288 net.cpp:165] Memory required for data: 23355560
I0926 21:27:25.930212 3110372288 layer_factory.hpp:77] Creating layer fc7
I0926 21:27:25.930244 3110372288 net.cpp:106] Creating Layer fc7
I0926 21:27:25.930274 3110372288 net.cpp:454] fc7 <- fc6
I0926 21:27:25.930310 3110372288 net.cpp:411] fc7 -> fc7
I0926 21:27:25.962868 3110372288 net.cpp:150] Setting up fc7
I0926 21:27:25.962956 3110372288 net.cpp:157] Top shape: 1 4096 (4096)
I0926 21:27:25.962991 3110372288 net.cpp:165] Memory required for data: 23371944
I0926 21:27:25.963037 3110372288 layer_factory.hpp:77] Creating layer silence_fc7
I0926 21:27:25.963112 3110372288 net.cpp:106] Creating Layer silence_fc7
I0926 21:27:25.963172 3110372288 net.cpp:454] silence_fc7 <- fc7
I0926 21:27:25.963210 3110372288 net.cpp:150] Setting up silence_fc7
I0926 21:27:25.963241 3110372288 net.cpp:165] Memory required for data: 23371944
I0926 21:27:25.963269 3110372288 net.cpp:228] silence_fc7 does not need backward computation.
I0926 21:27:25.963315 3110372288 net.cpp:228] fc7 does not need backward computation.
I0926 21:27:25.963348 3110372288 net.cpp:228] relu6 does not need backward computation.
I0926 21:27:25.963410 3110372288 net.cpp:228] fc6 does not need backward computation.
I0926 21:27:25.963455 3110372288 net.cpp:228] dummy_roi_pool_conv5 does not need backward computation.
I0926 21:27:25.963598 3110372288 net.cpp:226] rpn_loss_bbox needs backward computation.
I0926 21:27:25.963675 3110372288 net.cpp:226] rpn_loss_cls needs backward computation.
I0926 21:27:25.963714 3110372288 net.cpp:226] rpn-data needs backward computation.
I0926 21:27:25.963755 3110372288 net.cpp:226] rpn_cls_score_reshape needs backward computation.
I0926 21:27:25.963796 3110372288 net.cpp:226] rpn_bbox_pred needs backward computation.
I0926 21:27:25.963834 3110372288 net.cpp:226] rpn_cls_score_rpn_cls_score_0_split needs backward computation.
I0926 21:27:25.963874 3110372288 net.cpp:226] rpn_cls_score needs backward computation.
I0926 21:27:25.963910 3110372288 net.cpp:226] rpn_conv1_rpn_relu1_0_split needs backward computation.
I0926 21:27:25.963944 3110372288 net.cpp:226] rpn_relu1 needs backward computation.
I0926 21:27:25.963979 3110372288 net.cpp:226] rpn_conv1 needs backward computation.
I0926 21:27:25.964015 3110372288 net.cpp:226] relu5 needs backward computation.
I0926 21:27:25.964058 3110372288 net.cpp:226] conv5 needs backward computation.
I0926 21:27:25.964095 3110372288 net.cpp:226] relu4 needs backward computation.
I0926 21:27:25.964129 3110372288 net.cpp:226] conv4 needs backward computation.
I0926 21:27:25.964193 3110372288 net.cpp:226] relu3 needs backward computation.
I0926 21:27:25.964223 3110372288 net.cpp:226] conv3 needs backward computation.
I0926 21:27:25.964253 3110372288 net.cpp:226] pool2 needs backward computation.
I0926 21:27:25.964280 3110372288 net.cpp:226] norm2 needs backward computation.
I0926 21:27:25.964306 3110372288 net.cpp:226] relu2 needs backward computation.
I0926 21:27:25.964332 3110372288 net.cpp:226] conv2 needs backward computation.
I0926 21:27:25.964359 3110372288 net.cpp:226] pool1 needs backward computation.
I0926 21:27:25.964385 3110372288 net.cpp:226] norm1 needs backward computation.
I0926 21:27:25.964409 3110372288 net.cpp:226] relu1 needs backward computation.
I0926 21:27:25.964434 3110372288 net.cpp:226] conv1 needs backward computation.
I0926 21:27:25.964463 3110372288 net.cpp:228] data_input-data_0_split does not need backward computation.
I0926 21:27:25.964489 3110372288 net.cpp:228] input-data does not need backward computation.
I0926 21:27:25.964514 3110372288 net.cpp:270] This network produces output rpn_cls_loss
I0926 21:27:25.964540 3110372288 net.cpp:270] This network produces output rpn_loss_bbox
I0926 21:27:25.964587 3110372288 net.cpp:283] Network initialization done.
I0926 21:27:25.964679 3110372288 solver.cpp:60] Solver scaffolding done.
Loading pretrained model weights from data/imagenet_models/ZF.v2.caffemodel
I0926 21:27:26.130048 3110372288 net.cpp:816] Ignoring source layer data
I0926 21:27:26.131903 3110372288 net.cpp:816] Ignoring source layer conv5_relu5_0_split
I0926 21:27:26.131945 3110372288 net.cpp:816] Ignoring source layer roi_pool_conv5
I0926 21:27:26.150430 3110372288 net.cpp:816] Ignoring source layer drop6
I0926 21:27:26.159194 3110372288 net.cpp:816] Ignoring source layer relu7
I0926 21:27:26.159286 3110372288 net.cpp:816] Ignoring source layer drop7
I0926 21:27:26.159312 3110372288 net.cpp:816] Ignoring source layer fc7_drop7_0_split
I0926 21:27:26.159335 3110372288 net.cpp:816] Ignoring source layer cls_score
I0926 21:27:26.159399 3110372288 net.cpp:816] Ignoring source layer bbox_pred
I0926 21:27:26.159451 3110372288 net.cpp:816] Ignoring source layer loss_cls
I0926 21:27:26.159482 3110372288 net.cpp:816] Ignoring source layer loss_bbox
I0926 21:27:26.159852 3110372288 net.cpp:816] Ignoring source layer silence_rpn_cls_score
I0926 21:27:26.159898 3110372288 net.cpp:816] Ignoring source layer silence_rpn_bbox_pred
Solving...

python error

Process:               Python [21288]
Path:                  /usr/local/Cellar/python/2.7.13_1/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier:            Python
Version:               2.7.13 (2.7.13)
Code Type:             X86-64 (Native)
Parent Process:        Python [21256]
Responsible:           Python [21288]
User ID:               501

Date/Time:             2017-09-26 21:49:13.131 +0800
OS Version:            Mac OS X 10.12.6 (16G29)
Report Version:        12
Anonymous UUID:        178DEF6A-1A08-79ED-BBCB-131A4E4E6DFD

Sleep/Wake UUID:       B9C8F5F1-A14F-4403-9211-B7D9495EC662

Time Awake Since Boot: 88000 seconds
Time Since Wake:       5500 seconds

System Integrity Protection: enabled

Crashed Thread:        0  Dispatch queue: com.apple.main-thread

Exception Type:        EXC_BAD_ACCESS (SIGSEGV)
Exception Codes:       KERN_INVALID_ADDRESS at 0x0000000000000110
Exception Note:        EXC_CORPSE_NOTIFY

Termination Signal:    Segmentation fault: 11
Termination Reason:    Namespace SIGNAL, Code 0xb
Terminating Process:   exc handler [0]

VM Regions Near 0x110:
--> 
    __TEXT                 000000010d29f000-000000010d2a1000 [    8K] r-x/rwx SM=COW  /usr/local/Cellar/python/2.7.13_1/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python

Application Specific Information:
*** multi-threaded process forked ***
crashed on child side of fork pre-exec

Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0   libdispatch.dylib               0x00007fffb0715041 _dispatch_root_queue_push_override + 204
1   libxpc.dylib                    0x00007fffb09927ae _xpc_connection_activate_if_needed + 227
2   libxpc.dylib                    0x00007fffb09855b2 xpc_connection_resume + 64
3   libCoreVMClient.dylib           0x00007fff9f3faa1c cvms_connection_create + 216
4   libCVMSPluginSupport.dylib      0x00007fff9f3f4533 cvmPreInit + 554
5   libGFXShared.dylib              0x00007fff9f4015b1 gfxInitializeLibrary + 1802
6   com.apple.opencl                0x00007fff9f39e727 0x7fff9f37e000 + 132903
7   libsystem_pthread.dylib         0x00007fffb094db8c __pthread_once_handler + 65
8   libsystem_platform.dylib        0x00007fffb0942ac1 _os_once + 36
9   libsystem_pthread.dylib         0x00007fffb094db2b pthread_once + 57
10  com.apple.opencl                0x00007fff9f39e4da 0x7fff9f37e000 + 132314
11  com.apple.opencl                0x00007fff9f39eb66 clGetDeviceIDs + 182
12  libopencv_core.3.3.dylib        0x000000010f0eae62 cv::ocl::Context::Impl::setDefault() + 3730
13  libopencv_core.3.3.dylib        0x000000010f0e9cc0 cv::ocl::Context::getDefault(bool) + 156
14  libopencv_core.3.3.dylib        0x000000010f0e7cf8 cv::ocl::Device::getDefault() + 16
15  libopencv_core.3.3.dylib        0x000000010f0e7c92 cv::ocl::useOpenCL() + 43
16  libopencv_imgproc.3.3.dylib     0x0000000110248fb5 cv::resize(cv::_InputArray const&, cv::_OutputArray const&, cv::Size_<int>, double, double, int) + 730
17  cv2.so                          0x0000000124fb1b95 pyopencv_cv_resize(_object*, _object*, _object*) + 679
18  org.python.python               0x000000010d327c8e PyEval_EvalFrameEx + 11464
19  org.python.python               0x000000010d32db84 fast_function + 196
20  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
21  org.python.python               0x000000010d32db84 fast_function + 196
22  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
23  org.python.python               0x000000010d32db84 fast_function + 196
24  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
25  org.python.python               0x000000010d32db84 fast_function + 196
26  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
27  org.python.python               0x000000010d324de5 PyEval_EvalCodeEx + 1579
28  org.python.python               0x000000010d2cb651 function_call + 330
29  org.python.python               0x000000010d2adcda PyObject_Call + 97
30  org.python.python               0x000000010d2b84d5 instancemethod_call + 163
31  org.python.python               0x000000010d2adcda PyObject_Call + 97
32  org.python.python               0x000000010d32d528 PyEval_CallObjectWithKeywords + 159
33  org.python.python               0x000000010d341be6 PyEval_CallFunction + 170
34  _caffe.so                       0x000000010e7eea60 boost::python::detail::returnable<boost::python::api::object>::type boost::python::call<boost::python::api::object, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> >, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > >(_object*, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&, boost::type<boost::python::api::object>*) + 96
35  _caffe.so                       0x000000010e7ee8e5 boost::python::detail::dependent<boost::python::api::object, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > >::type boost::python::api::object_operators<boost::python::api::proxy<boost::python::api::attribute_policies> >::operator()<std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> >, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > >(std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&) const + 53
36  _caffe.so                       0x000000010e7edb0b caffe::PythonLayer<float>::Forward_cpu(std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&) + 59
37  libcaffe.so.1.0.0-rc3           0x000000010e9b9dde caffe::Layer<float>::Forward(std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&) + 110
38  libcaffe.so.1.0.0-rc3           0x000000010ea3cbe1 caffe::Net<float>::ForwardFromTo(int, int) + 257
39  libcaffe.so.1.0.0-rc3           0x000000010ea3d278 caffe::Net<float>::Forward(std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&, float*) + 120
40  libcaffe.so.1.0.0-rc3           0x000000010ea5302e caffe::Solver<float>::Step(int) + 558
41  _caffe.so                       0x000000010e7f2146 boost::python::detail::caller_arity<2u>::impl<void (caffe::Solver<float>::*)(int), boost::python::default_call_policies, boost::mpl::vector3<void, caffe::Solver<float>&, int> >::operator()(_object*, _object*) + 166
42  libboost_python.dylib           0x0000000114081346 boost::python::objects::function::call(_object*, _object*) const + 822
43  libboost_python.dylib           0x0000000114083eba boost::detail::function::void_function_ref_invoker0<boost::python::objects::(anonymous namespace)::bind_return, void>::invoke(boost::detail::function::function_buffer&) + 26
44  libboost_python.dylib           0x0000000114089d8e boost::python::handle_exception_impl(boost::function0<void>) + 78
45  libboost_python.dylib           0x00000001140839f3 boost::python::objects::function_call(_object*, _object*, _object*) + 83
46  org.python.python               0x000000010d2adcda PyObject_Call + 97
47  org.python.python               0x000000010d327b96 PyEval_EvalFrameEx + 11216
48  org.python.python               0x000000010d32db84 fast_function + 196
49  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
50  org.python.python               0x000000010d324de5 PyEval_EvalCodeEx + 1579
51  org.python.python               0x000000010d32dbe2 fast_function + 290
52  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
53  org.python.python               0x000000010d324de5 PyEval_EvalCodeEx + 1579
54  org.python.python               0x000000010d2cb651 function_call + 330
55  org.python.python               0x000000010d2adcda PyObject_Call + 97
56  org.python.python               0x000000010d32a12f PyEval_EvalFrameEx + 20841
57  org.python.python               0x000000010d32db84 fast_function + 196
58  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
59  org.python.python               0x000000010d32db84 fast_function + 196
60  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
61  org.python.python               0x000000010d324de5 PyEval_EvalCodeEx + 1579
62  org.python.python               0x000000010d2cb651 function_call + 330
63  org.python.python               0x000000010d2adcda PyObject_Call + 97
64  org.python.python               0x000000010d2b84d5 instancemethod_call + 163
65  org.python.python               0x000000010d2adcda PyObject_Call + 97
66  org.python.python               0x000000010d2f3df7 slot_tp_init + 64
67  org.python.python               0x000000010d2f0fff type_call + 182
68  org.python.python               0x000000010d2adcda PyObject_Call + 97
69  org.python.python               0x000000010d327b96 PyEval_EvalFrameEx + 11216
70  org.python.python               0x000000010d32db84 fast_function + 196
71  org.python.python               0x000000010d327ae8 PyEval_EvalFrameEx + 11042
72  org.python.python               0x000000010d324de5 PyEval_EvalCodeEx + 1579
73  org.python.python               0x000000010d3247b4 PyEval_EvalCode + 32
74  org.python.python               0x000000010d345fdf run_mod + 49
75  org.python.python               0x000000010d346086 PyRun_FileExFlags + 130
76  org.python.python               0x000000010d345c03 PyRun_SimpleFileExFlags + 697
77  org.python.python               0x000000010d356ee5 Py_Main + 3013
78  libdyld.dylib                   0x00007fffb0734235 start + 1

Thread 0 crashed with X86 Thread State (64-bit):
  rax: 0x00000001208018c0  rbx: 0x0000000000000480  rcx: 0x0000000000000100  rdx: 0x0000000000000001
  rdi: 0x0000000120800000  rsi: 0x0000000120800080  rbp: 0x00007fff5295a7e0  rsp: 0x00007fff5295a7c0
   r8: 0x00000001208018c0   r9: 0x0000000120800000  r10: 0x0000000100000000  r11: 0x0000000c00000000
  r12: 0x00007fffb9617e40  r13: 0x00007fff5295ad40  r14: 0x00007fffb96181c0  r15: 0x0000000120801880
  rip: 0x00007fffb0715041  rfl: 0x0000000000010206  cr2: 0x0000000000000110

Logical CPU:     1
Error Code:      0x00000006
Trap Number:     14

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    0x7fffb0638000 -     0x7fffb0649ff3  libz.1.dylib (67) <46E3FFA2-4328-327A-8D34-A03E20BFFB8E> /usr/lib/libz.1.dylib
    0x7fffb0658000 -     0x7fffb065cff7  libcache.dylib (79) <093A4DAB-8385-3D47-A350-E20CB7CCF7BF> /usr/lib/system/libcache.dylib
    0x7fffb065d000 -     0x7fffb0667fff  libcommonCrypto.dylib (60092.50.5) <8A64D1B0-C70E-385C-92F0-E669079FDA90> /usr/lib/system/libcommonCrypto.dylib
    0x7fffb0668000 -     0x7fffb066ffff  libcompiler_rt.dylib (62) <55D47421-772A-32AB-B529-1A46C2F43B4D> /usr/lib/system/libcompiler_rt.dylib
    0x7fffb0670000 -     0x7fffb0678fff  libcopyfile.dylib (138) <819BEA3C-DF11-3E3D-A1A1-5A51C5BF1961> /usr/lib/system/libcopyfile.dylib
    0x7fffb0679000 -     0x7fffb06fcfdf  libcorecrypto.dylib (442.50.19) <65D7165E-2E71-335D-A2D6-33F78E2DF0C1> /usr/lib/system/libcorecrypto.dylib
    0x7fffb06fd000 -     0x7fffb072efff  libdispatch.dylib (703.50.37) <6582BAD6-ED27-3B30-B620-90B1C5A4AE3C> /usr/lib/system/libdispatch.dylib
    0x7fffb072f000 -     0x7fffb0734ffb  libdyld.dylib (433.5) <9B2AC56D-107C-3541-A127-9094A751F2C9> /usr/lib/system/libdyld.dylib
    0x7fffb0735000 -     0x7fffb0735ffb  libkeymgr.dylib (28) <7AA011A9-DC21-3488-BF73-3B5B14D1FDD6> /usr/lib/system/libkeymgr.dylib
    0x7fffb0736000 -     0x7fffb0742fff  libkxld.dylib (3789.70.16) <BD02EF3F-6E09-3A64-AB21-553D5B1D2859> /usr/lib/system/libkxld.dylib
    0x7fffb0743000 -     0x7fffb0743fff  liblaunch.dylib (972.70.1) <B856ABD2-896E-3DE0-B2C8-146A6AF8E2A7> /usr/lib/system/liblaunch.dylib
    0x7fffb0744000 -     0x7fffb0749ff3  libmacho.dylib (898) <17D5D855-F6C3-3B04-B680-E9BF02EF8AED> /usr/lib/system/libmacho.dylib
    0x7fffb074a000 -     0x7fffb074cff3  libquarantine.dylib (85.50.1) <12448CC2-378E-35F3-BE33-9DC395A5B970> /usr/lib/system/libquarantine.dylib
    0x7fffb074d000 -     0x7fffb074effb  libremovefile.dylib (45) <38D4CB9C-10CD-30D3-8B7B-A515EC75FE85> /usr/lib/system/libremovefile.dylib
    0x7fffb074f000 -     0x7fffb0767ff7  libsystem_asl.dylib (349.50.5) <096E4228-3B7C-30A6-8B13-EC909A64499A> /usr/lib/system/libsystem_asl.dylib
    0x7fffb0768000 -     0x7fffb0768ff7  libsystem_blocks.dylib (67) <10DC5404-73AB-35B3-A277-A8AFECB476EB> /usr/lib/system/libsystem_blocks.dylib
    0x7fffb0769000 -     0x7fffb07f6fef  libsystem_c.dylib (1158.50.2) <E5AE5244-7D0C-36AC-8BB6-C7AE7EA52A4B> /usr/lib/system/libsystem_c.dylib
    0x7fffb07f7000 -     0x7fffb07faffb  libsystem_configuration.dylib (888.60.2) <BECC01A2-CA8D-31E6-BCDF-D452965FA976> /usr/lib/system/libsystem_configuration.dylib
    0x7fffb07fb000 -     0x7fffb07fefff  libsystem_coreservices.dylib (41.4) <7D26DE79-B424-3450-85E1-F7FAB32714AB> /usr/lib/system/libsystem_coreservices.dylib
    0x7fffb07ff000 -     0x7fffb0817fff  libsystem_coretls.dylib (121.50.4) <EC6FCF07-DCFB-3A03-9CC9-6DD3709974C6> /usr/lib/system/libsystem_coretls.dylib
    0x7fffb0818000 -     0x7fffb081efff  libsystem_dnssd.dylib (765.50.9) <CC960215-0B1B-3822-A13A-3DDE96FA796F> /usr/lib/system/libsystem_dnssd.dylib
    0x7fffb081f000 -     0x7fffb0848ff7  libsystem_info.dylib (503.50.4) <611DB84C-BF70-3F92-8702-B9F28A900920> /usr/lib/system/libsystem_info.dylib
    0x7fffb0849000 -     0x7fffb086bff7  libsystem_kernel.dylib (3789.70.16) <34B1F16C-BC9C-3C5F-9045-0CAE91CB5914> /usr/lib/system/libsystem_kernel.dylib
    0x7fffb086c000 -     0x7fffb08b3fe7  libsystem_m.dylib (3121.6) <86D499B5-BBDC-3D3B-8A4E-97AE8E6672A4> /usr/lib/system/libsystem_m.dylib
    0x7fffb08b4000 -     0x7fffb08d2ff7  libsystem_malloc.dylib (116.50.8) <A3D15F17-99A6-3367-8C7E-4280E8619C95> /usr/lib/system/libsystem_malloc.dylib
    0x7fffb08d3000 -     0x7fffb092cffb  libsystem_network.dylib (856.60.1) <369D0221-56CA-3C3E-9EDE-94B41CAE77B7> /usr/lib/system/libsystem_network.dylib
    0x7fffb092d000 -     0x7fffb0936ff3  libsystem_networkextension.dylib (563.60.2) <B021F2B3-8A75-3633-ABB0-FC012B8E9B0C> /usr/lib/system/libsystem_networkextension.dylib
    0x7fffb0937000 -     0x7fffb0940ff3  libsystem_notify.dylib (165.20.1) <B8160190-A069-3B3A-BDF6-2AA408221FAE> /usr/lib/system/libsystem_notify.dylib
    0x7fffb0941000 -     0x7fffb0949fe7  libsystem_platform.dylib (126.50.8) <897462FD-B318-321B-A554-E61982630F7E> /usr/lib/system/libsystem_platform.dylib
    0x7fffb094a000 -     0x7fffb0954ff7  libsystem_pthread.dylib (218.60.3) <B8FB5E20-3295-39E2-B5EB-B464D1D4B104> /usr/lib/system/libsystem_pthread.dylib
    0x7fffb0955000 -     0x7fffb0958ff7  libsystem_sandbox.dylib (592.70.1) <4B92EC49-ACD0-36AE-B07A-A2B8152EAF9D> /usr/lib/system/libsystem_sandbox.dylib
    0x7fffb0959000 -     0x7fffb095aff3  libsystem_secinit.dylib (24.50.4) <F78B847B-3565-3E4B-98A6-F7AD40392E2D> /usr/lib/system/libsystem_secinit.dylib
    0x7fffb095b000 -     0x7fffb0962ffb  libsystem_symptoms.dylib (532.50.47) <3390E07C-C1CE-348F-ADBD-2C5440B45EAA> /usr/lib/system/libsystem_symptoms.dylib
    0x7fffb0963000 -     0x7fffb0976ff7  libsystem_trace.dylib (518.70.1) <AC63A7FE-50D9-3A30-96E6-F6B7FF16E465> /usr/lib/system/libsystem_trace.dylib
    0x7fffb0977000 -     0x7fffb097cffb  libunwind.dylib (35.3) <3D50D8A8-C460-334D-A519-2DA841102C6B> /usr/lib/system/libunwind.dylib
    0x7fffb097d000 -     0x7fffb09a6ff7  libxpc.dylib (972.70.1) <BF896DF0-D8E9-31A8-A4B3-01120BFEEE52> /usr/lib/system/libxpc.dylib

External Modification Summary:
  Calls made by other processes targeting this process:
    task_for_pid: 0
    thread_create: 0
    thread_set_state: 0
  Calls made by this process:
    task_for_pid: 0
    thread_create: 0
    thread_set_state: 0
  Calls made by all processes on this machine:
    task_for_pid: 53754
    thread_create: 0
    thread_set_state: 0

VM Region Summary:
ReadOnly portion of Libraries: Total=507.6M resident=0K(0%) swapped_out_or_unallocated=507.6M(100%)
Writable regions: Total=630.4M written=0K(0%) resident=0K(0%) swapped_out=0K(0%) unallocated=630.4M(100%)
squidgy123 commented 6 years ago

I am having exactly the same issue - did you manage to solve it?

cobbwho commented 6 years ago

@andyu78945 Hi. Did you solved it. Now I have the same problem.