faster rcnn训练的时候应该是哪个层出了问题

Question

• echo Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-04-19_01-16-47 Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-04-19_01-16-47

• ./tools/train_faster_rcnn_alt_opt.py --gpu 0 --net_name ZF --weights data/imagenet_models/CaffeNet.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', gpu_id=0, imdb_name='voc_2007_trainval', net_name='ZF', pretrained_model='data/imagenet_models/CaffeNet.v2.caffemodel', set_cfgs=None)

  Stage 1 RPN, init from ImageNet model
  

  Init model: data/imagenet_models/CaffeNet.v2.caffemodel
  Using config:
  {'DATA_DIR': 'E:\caffe-frcnn\py-faster-rcnn-master\data',
  'DEDUP_BOXES': 0.0625,
  'EPS': 1e-14,
  'EXP_DIR': 'default',
  'GPU_ID': 0,
  'MATLAB': 'matlab',
  'MODELS_DIR': 'E:\caffe-frcnn\py-faster-rcnn-master\models\pascal_voc',
  'PIXEL_MEANS': array([[[ 102.9801, 115.9465, 122.7717]]]),
  'RNG_SEED': 3,
  'ROOT_DIR': 'E:\caffe-frcnn\py-faster-rcnn-master',
  'TEST': {'BBOX_REG': True,
  'HAS_RPN': False,
  'MAX_SIZE': 1000,
  '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': [600],
  'SVM': False},
  'TRAIN': {'ASPECT_GROUPING': True,
  'BATCH_SIZE': 128,
  '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': 0.1,
  'FG_FRACTION': 0.25,
  'FG_THRESH': 0.5,
  'HAS_RPN': True,
  'IMS_PER_BATCH': 1,
  'MAX_SIZE': 1000,
  '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': [600],
  'SNAPSHOT_INFIX': '',
  'SNAPSHOT_ITERS': 10000,
  'USE_FLIPPED': True,
  'USE_PREFETCH': False},
  'USE_GPU_NMS': True}
  Loaded dataset voc_2007_trainval for training
  Set proposal method: gt
  Appending horizontally-flipped training examples...
  voc_2007_trainval gt roidb loaded from E:\caffe-frcnn\py-faster-rcnn-master\data\cache\voc_2007_trainval_gt_roidb.pkl
  done
  Preparing training data...
  done
  roidb len: 100
  Output will be saved to E:\caffe-frcnn\py-faster-rcnn-master\output\default\voc_2007_trainval
  Filtered 0 roidb entries: 100 -> 100
  WARNING: Logging before InitGoogleLogging() is written to STDERR
  I0419 01:16:54.964942 25240 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
  I0419 01:16:55.073168 25240 solver.cpp:44] 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: 60000
  snapshot: 0
  snapshot_prefix: "zf_rpn"
  average_loss: 100
  I0419 01:16:55.073168 25240 solver.cpp:77] Creating training net from train_net file: models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt
  I0419 01:16:55.074168 25240 net.cpp:51] 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\': 2"
  }
  }
  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"RoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1}

  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"
  }
  I0419 01:16:55.074668 25240 layer_factory.cpp:58] Creating layer input-data
  I0419 01:16:55.109673 25240 net.cpp:84] Creating Layer input-data
  I0419 01:16:55.109673 25240 net.cpp:380] input-data -> data
  I0419 01:16:55.109673 25240 net.cpp:380] input-data -> im_info
  I0419 01:16:55.109673 25240 net.cpp:380] input-data -> gt_boxes
  I0419 01:16:55.111171 25240 net.cpp:122] Setting up input-data
  I0419 01:16:55.111171 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000)
  I0419 01:16:55.111171 25240 net.cpp:129] Top shape: 1 3 (3)
  I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 4 (4)
  I0419 01:16:55.111668 25240 net.cpp:137] Memory required for data: 7200028
  I0419 01:16:55.111668 25240 layer_factory.cpp:58] Creating layer data_input-data_0_split
  I0419 01:16:55.111668 25240 net.cpp:84] Creating Layer data_input-data_0_split
  I0419 01:16:55.111668 25240 net.cpp:406] data_input-data_0_split <- data
  I0419 01:16:55.111668 25240 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_0
  I0419 01:16:55.111668 25240 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_1
  I0419 01:16:55.111668 25240 net.cpp:122] Setting up data_input-data_0_split
  I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000)
  I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000)
  I0419 01:16:55.111668 25240 net.cpp:137] Memory required for data: 21600028
  I0419 01:16:55.111668 25240 layer_factory.cpp:58] Creating layer conv1
  I0419 01:16:55.111668 25240 net.cpp:84] Creating Layer conv1
  I0419 01:16:55.111668 25240 net.cpp:406] conv1 <- data_input-data_0_split_0
  I0419 01:16:55.111668 25240 net.cpp:380] conv1 -> conv1
  I0419 01:16:55.577394 25240 net.cpp:122] Setting up conv1
  I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000)
  I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 79200028
  I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer relu1
  I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer relu1
  I0419 01:16:55.577394 25240 net.cpp:406] relu1 <- conv1
  I0419 01:16:55.577394 25240 net.cpp:367] relu1 -> conv1 (in-place)
  I0419 01:16:55.577394 25240 net.cpp:122] Setting up relu1
  I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000)
  I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 136800028
  I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer norm1
  I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer norm1
  I0419 01:16:55.577394 25240 net.cpp:406] norm1 <- conv1
  I0419 01:16:55.577394 25240 net.cpp:380] norm1 -> norm1
  I0419 01:16:55.577394 25240 net.cpp:122] Setting up norm1
  I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000)
  I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 194400028
  I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer pool1
  I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer pool1
  I0419 01:16:55.577394 25240 net.cpp:406] pool1 <- norm1
  I0419 01:16:55.577394 25240 net.cpp:380] pool1 -> pool1
  I0419 01:16:55.577394 25240 net.cpp:122] Setting up pool1
  I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 151 251 (3638496)
  I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 208954012
  I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer conv2
  I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer conv2
  I0419 01:16:55.577394 25240 net.cpp:406] conv2 <- pool1
  I0419 01:16:55.577394 25240 net.cpp:380] conv2 -> conv2
  I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv2
  I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456)
  I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 218759836
  I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu2
  I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu2
  I0419 01:16:55.593016 25240 net.cpp:406] relu2 <- conv2
  I0419 01:16:55.593016 25240 net.cpp:367] relu2 -> conv2 (in-place)
  I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu2
  I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456)
  I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 228565660
  I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer norm2
  I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer norm2
  I0419 01:16:55.593016 25240 net.cpp:406] norm2 <- conv2
  I0419 01:16:55.593016 25240 net.cpp:380] norm2 -> norm2
  I0419 01:16:55.593016 25240 net.cpp:122] Setting up norm2
  I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456)
  I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 238371484
  I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer pool2
  I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer pool2
  I0419 01:16:55.593016 25240 net.cpp:406] pool2 <- norm2
  I0419 01:16:55.593016 25240 net.cpp:380] pool2 -> pool2
  I0419 01:16:55.593016 25240 net.cpp:122] Setting up pool2
  I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 39 64 (638976)
  I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 240927388
  I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv3
  I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv3
  I0419 01:16:55.593016 25240 net.cpp:406] conv3 <- pool2
  I0419 01:16:55.593016 25240 net.cpp:380] conv3 -> conv3
  I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv3
  I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464)
  I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 244761244
  I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu3
  I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu3
  I0419 01:16:55.593016 25240 net.cpp:406] relu3 <- conv3
  I0419 01:16:55.593016 25240 net.cpp:367] relu3 -> conv3 (in-place)
  I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu3
  I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464)
  I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 248595100
  I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv4
  I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv4
  I0419 01:16:55.593016 25240 net.cpp:406] conv4 <- conv3
  I0419 01:16:55.593016 25240 net.cpp:380] conv4 -> conv4
  I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv4
  I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464)
  I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 252428956
  I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu4
  I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu4
  I0419 01:16:55.593016 25240 net.cpp:406] relu4 <- conv4
  I0419 01:16:55.593016 25240 net.cpp:367] relu4 -> conv4 (in-place)
  I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu4
  I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464)
  I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 256262812
  I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv5
  I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv5
  I0419 01:16:55.593016 25240 net.cpp:406] conv5 <- conv4
  I0419 01:16:55.593016 25240 net.cpp:380] conv5 -> conv5
  I0419 01:16:55.608644 25240 net.cpp:122] Setting up conv5
  I0419 01:16:55.608644 25240 net.cpp:129] Top shape: 1 256 39 64 (638976)
  I0419 01:16:55.608644 25240 net.cpp:137] Memory required for data: 258818716
  I0419 01:16:55.608644 25240 layer_factory.cpp:58] Creating layer relu5
  I0419 01:16:55.608644 25240 net.cpp:84] Creating Layer relu5
  I0419 01:16:55.608644 25240 net.cpp:406] relu5 <- conv5
  I0419 01:16:55.608644 25240 net.cpp:367] relu5 -> conv5 (in-place)
  I0419 01:16:55.608644 25240 net.cpp:122] Setting up relu5
  I0419 01:16:55.608644 25240 net.cpp:129] Top shape: 1 256 39 64 (638976)
  I0419 01:16:55.608644 25240 net.cpp:137] Memory required for data: 261374620
  I0419 01:16:55.608644 25240 layer_factory.cpp:58] Creating layer rpn_conv1
  I0419 01:16:55.608644 25240 net.cpp:84] Creating Layer rpn_conv1
  I0419 01:16:55.608644 25240 net.cpp:406] rpn_conv1 <- conv5
  I0419 01:16:55.608644 25240 net.cpp:380] rpn_conv1 -> rpn_conv1
  I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_conv1
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976)
  I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 263930524
  I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_relu1
  I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_relu1
  I0419 01:16:55.624267 25240 net.cpp:406] rpn_relu1 <- rpn_conv1
  I0419 01:16:55.624267 25240 net.cpp:367] rpn_relu1 -> rpn_conv1 (in-place)
  I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_relu1
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976)
  I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 266486428
  I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_conv1_rpn_relu1_0_split
  I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_conv1_rpn_relu1_0_split
  I0419 01:16:55.624267 25240 net.cpp:406] rpn_conv1_rpn_relu1_0_split <- rpn_conv1
  I0419 01:16:55.624267 25240 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_0
  I0419 01:16:55.624267 25240 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_1
  I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_conv1_rpn_relu1_0_split
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976)
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976)
  I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 271598236
  I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score
  I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score
  I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score <- rpn_conv1_rpn_relu1_0_split_0
  I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score -> rpn_cls_score
  I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928)
  I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 271777948
  I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score_rpn_cls_score_0_split
  I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score_rpn_cls_score_0_split
  I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score
  I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0
  I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1
  I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score_rpn_cls_score_0_split
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928)
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928)
  I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272137372
  I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_bbox_pred
  I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_bbox_pred
  I0419 01:16:55.624267 25240 net.cpp:406] rpn_bbox_pred <- rpn_conv1_rpn_relu1_0_split_1
  I0419 01:16:55.624267 25240 net.cpp:380] rpn_bbox_pred -> rpn_bbox_pred
  I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_bbox_pred
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 36 39 64 (89856)
  I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272496796
  I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score_reshape
  I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score_reshape
  I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0
  I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_reshape -> rpn_cls_score_reshape
  I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score_reshape
  I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 2 351 64 (44928)
  I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272676508
  I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn-data
  I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn-data
  I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1
  I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- gt_boxes
  I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- im_info
  I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- data_input-data_0_split_1
  I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_labels
  I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_targets
  I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_inside_weights
  I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_outside_weights
  I0419 01:16:55.639891 25240 net.cpp:122] Setting up rpn-data
  I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 1 351 64 (22464)
  I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856)
  I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856)
  I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856)
  I0419 01:16:55.639891 25240 net.cpp:137] Memory required for data: 273844636
  I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_cls
  I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn_loss_cls
  I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_cls <- rpn_cls_score_reshape
  I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_cls <- rpn_labels
  I0419 01:16:55.639891 25240 net.cpp:380] rpn_loss_cls -> rpn_cls_loss
  I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_cls
  I0419 01:16:55.639891 25240 net.cpp:122] Setting up rpn_loss_cls
  I0419 01:16:55.639891 25240 net.cpp:129] Top shape: (1)
  I0419 01:16:55.639891 25240 net.cpp:132] with loss weight 1
  I0419 01:16:55.639891 25240 net.cpp:137] Memory required for data: 273844640
  I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_bbox
  I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn_loss_bbox
  I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_bbox <- rpn_bbox_pred
  I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_bbox <- rpn_bbox_targets
  I0419 01:16:55.639891 2*** Check failure stack trace: ***