{"action":"opened","issue":{"url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/694","repository_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn","labels_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/694/labels{/name}","comments_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/694/comments","events_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/694/events","html_url":"https://github.com/rbgirshick/py-faster-rcnn/issues/694","id":260638518,"number":694,"title":"Faster rcnn zf trainSolving...python quits unexpectedly error","user":{"login":"andyu78945","id":32301397,"avatar_url":"https://avatars1.githubusercontent.com/u/32301397?v=4","gravatar_id":"","url":"https://api.github.com/users/andyu78945","html_url":"https://github.com/andyu78945","followers_url":"https://api.github.com/users/andyu78945/followers","following_url":"https://api.github.com/users/andyu78945/following{/other_user}","gists_url":"https://api.github.com/users/andyu78945/gists{/gist_id}","starred_url":"https://api.github.com/users/andyu78945/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/andyu78945/subscriptions","organizations_url":"https://api.github.com/users/andyu78945/orgs","repos_url":"https://api.github.com/users/andyu78945/repos","events_url":"https://api.github.com/users/andyu78945/events{/privacy}","received_events_url":"https://api.github.com/users/andyu78945/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2017-09-26T14:11:20Z","updated_at":"2017-09-26T14:11:20Z","closed_at":null,"author_association":"NONE","body":"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.\r\n```\r\nLiude-MacBook-Pro:~ andyu78945$ cd /Users/andyu78945/py-faster-rcnn/\r\nLiude-MacBook-Pro:py-faster-rcnn andyu78945$ ./experiments/scripts/faster_rcnn_alt_opt.sh 0 ZF pascal_voc\r\n+ set -e\r\n+ export PYTHONUNBUFFERED=True\r\n+ PYTHONUNBUFFERED=True\r\n+ GPU_ID=0\r\n+ NET=ZF\r\n+ NET_lc=ZF\r\n+ DATASET=pascal_voc\r\n+ array=($@)\r\n+ len=3\r\n+ EXTRA_ARGS=\r\n+ EXTRA_ARGS_SLUG=\r\n+ case $DATASET in\r\n+ TRAIN_IMDB=voc_2007_trainval\r\n+ TEST_IMDB=voc_2007_test\r\n+ PT_DIR=pascal_voc\r\n+ ITERS=10000\r\n++ date +%Y-%m-%d_%H-%M-%S\r\n+ LOG=experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-09-26_21-27-24\r\n+ exec\r\n++ tee -a experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-09-26_21-27-24\r\n+ echo Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-09-26_21-27-24\r\nLogging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-09-26_21-27-24\r\n+ ./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\r\nCalled with args:\r\nNamespace(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)\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\nStage 1 RPN, init from ImageNet model\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\nInit model: data/imagenet_models/ZF.v2.caffemodel\r\nUsing config:\r\n{'DATA_DIR': '/Users/andyu78945/py-faster-rcnn/data',\r\n 'DEDUP_BOXES': 0.0625,\r\n 'EPS': 1e-14,\r\n 'EXP_DIR': 'faster_rcnn_alt_opt',\r\n 'GPU_ID': 0,\r\n 'MATLAB': 'matlab',\r\n 'MODELS_DIR': '/Users/andyu78945/py-faster-rcnn/models/pascal_voc',\r\n 'PIXEL_MEANS': array([[[ 102.9801,  115.9465,  122.7717]]]),\r\n 'RNG_SEED': 3,\r\n 'ROOT_DIR': '/Users/andyu78945/py-faster-rcnn',\r\n 'TEST': {'BBOX_REG': True,\r\n          'HAS_RPN': True,\r\n          'MAX_SIZE': 256,\r\n          'NMS': 0.3,\r\n          'PROPOSAL_METHOD': 'selective_search',\r\n          'RPN_MIN_SIZE': 16,\r\n          'RPN_NMS_THRESH': 0.7,\r\n          'RPN_POST_NMS_TOP_N': 300,\r\n          'RPN_PRE_NMS_TOP_N': 6000,\r\n          'SCALES': [224],\r\n          'SVM': False},\r\n 'TRAIN': {'ASPECT_GROUPING': True,\r\n           'BATCH_SIZE': 32,\r\n           'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],\r\n           'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0],\r\n           'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2],\r\n           'BBOX_NORMALIZE_TARGETS': True,\r\n           'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': False,\r\n           'BBOX_REG': False,\r\n           'BBOX_THRESH': 0.5,\r\n           'BG_THRESH_HI': 0.5,\r\n           'BG_THRESH_LO': 0.0,\r\n           'FG_FRACTION': 0.25,\r\n           'FG_THRESH': 0.5,\r\n           'HAS_RPN': True,\r\n           'IMS_PER_BATCH': 1,\r\n           'MAX_SIZE': 224,\r\n           'PROPOSAL_METHOD': 'gt',\r\n           'RPN_BATCHSIZE': 256,\r\n           'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],\r\n           'RPN_CLOBBER_POSITIVES': False,\r\n           'RPN_FG_FRACTION': 0.5,\r\n           'RPN_MIN_SIZE': 16,\r\n           'RPN_NEGATIVE_OVERLAP': 0.3,\r\n           'RPN_NMS_THRESH': 0.7,\r\n           'RPN_POSITIVE_OVERLAP': 0.7,\r\n           'RPN_POSITIVE_WEIGHT': -1.0,\r\n           'RPN_POST_NMS_TOP_N': 2000,\r\n           'RPN_PRE_NMS_TOP_N': 12000,\r\n           'SCALES': [224],\r\n           'SNAPSHOT_INFIX': 'stage1',\r\n           'SNAPSHOT_ITERS': 30000,\r\n           'USE_FLIPPED': True,\r\n           'USE_PREFETCH': False},\r\n 'USE_GPU_NMS': False}\r\nLoaded dataset `voc_2007_trainval` for training\r\nSet proposal method: gt\r\nAppending horizontally-flipped training examples...\r\nwrote gt roidb to /Users/andyu78945/py-faster-rcnn/data/cache/voc_2007_trainval_gt_roidb.pkl\r\ndone\r\nPreparing training data...\r\ndone\r\nroidb len: 160\r\nOutput will be saved to `/Users/andyu78945/py-faster-rcnn/output/faster_rcnn_alt_opt/voc_2007_trainval`\r\nFiltered 0 roidb entries: 160 -> 160\r\nWARNING: Logging before InitGoogleLogging() is written to STDERR\r\nI0926 21:27:25.822978 3110372288 solver.cpp:48] Initializing solver from parameters: \r\ntrain_net: \"models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt\"\r\nbase_lr: 0.001\r\ndisplay: 20\r\nlr_policy: \"step\"\r\ngamma: 0.1\r\nmomentum: 0.9\r\nweight_decay: 0.0005\r\nstepsize: 10000\r\nsnapshot: 0\r\nsnapshot_prefix: \"zf_rpn\"\r\naverage_loss: 100\r\nI0926 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\r\nI0926 21:27:25.823637 3110372288 net.cpp:49] Initializing net from parameters: \r\nname: \"ZF\"\r\nstate {\r\n  phase: TRAIN\r\n}\r\nlayer {\r\n  name: \"input-data\"\r\n  type: \"Python\"\r\n  top: \"data\"\r\n  top: \"im_info\"\r\n  top: \"gt_boxes\"\r\n  python_param {\r\n    module: \"roi_data_layer.layer\"\r\n    layer: \"RoIDataLayer\"\r\n    param_str: \"\\'num_classes\\': 6\"\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv1\"\r\n  type: \"Convolution\"\r\n  bottom: \"data\"\r\n  top: \"conv1\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  convolution_param {\r\n    num_output: 96\r\n    pad: 3\r\n    kernel_size: 7\r\n    stride: 2\r\n  }\r\n}\r\nlayer {\r\n  name: \"relu1\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv1\"\r\n  top: \"conv1\"\r\n}\r\nlayer {\r\n  name: \"norm1\"\r\n  type: \"LRN\"\r\n  bottom: \"conv1\"\r\n  top: \"norm1\"\r\n  lrn_param {\r\n    local_size: 3\r\n    alpha: 5e-05\r\n    beta: 0.75\r\n    norm_region: WITHIN_CHANNEL\r\n    engine: CAFFE\r\n  }\r\n}\r\nlayer {\r\n  name: \"pool1\"\r\n  type: \"Pooling\"\r\n  bottom: \"norm1\"\r\n  top: \"pool1\"\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 3\r\n    stride: 2\r\n    pad: 1\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2\"\r\n  type: \"Convolution\"\r\n  bottom: \"pool1\"\r\n  top: \"conv2\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    pad: 2\r\n    kernel_size: 5\r\n    stride: 2\r\n  }\r\n}\r\nlayer {\r\n  name: \"relu2\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv2\"\r\n  top: \"conv2\"\r\n}\r\nlayer {\r\n  name: \"norm2\"\r\n  type: \"LRN\"\r\n  bottom: \"conv2\"\r\n  top: \"norm2\"\r\n  lrn_param {\r\n    local_size: 3\r\n    alpha: 5e-05\r\n    beta: 0.75\r\n    norm_region: WITHIN_CHANNEL\r\n    engine: CAFFE\r\n  }\r\n}\r\nlayer {\r\n  name: \"pool2\"\r\n  type: \"Pooling\"\r\n  bottom: \"norm2\"\r\n  top: \"pool2\"\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 3\r\n    stride: 2\r\n    pad: 1\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3\"\r\n  type: \"Convolution\"\r\n  bottom: \"pool2\"\r\n  top: \"conv3\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  convolution_param {\r\n    num_output: 384\r\n    pad: 1\r\n    kernel_size: 3\r\n    stride: 1\r\n  }\r\n}\r\nlayer {\r\n  name: \"relu3\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3\"\r\n  top: \"conv3\"\r\n}\r\nlayer {\r\n  name: \"conv4\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3\"\r\n  top: \"conv4\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  convolution_param {\r\n    num_output: 384\r\n    pad: 1\r\n    kernel_size: 3\r\n    stride: 1\r\n  }\r\n}\r\nlayer {\r\n  name: \"relu4\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4\"\r\n  top: \"conv4\"\r\n}\r\nlayer {\r\n  name: \"conv5\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4\"\r\n  top: \"conv5\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    pad: 1\r\n    kernel_size: 3\r\n    stride: 1\r\n  }\r\n}\r\nlayer {\r\n  name: \"relu5\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv5\"\r\n  top: \"conv5\"\r\n}\r\nlayer {\r\n  name: \"rpn_conv1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv5\"\r\n  top: \"rpn_conv1\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    pad: 1\r\n    kernel_size: 3\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"gaussian\"\r\n      std: 0.01\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"rpn_relu1\"\r\n  type: \"ReLU\"\r\n  bottom: \"rpn_conv1\"\r\n  top: \"rpn_conv1\"\r\n}\r\nlayer {\r\n  name: \"rpn_cls_score\"\r\n  type: \"Convolution\"\r\n  bottom: \"rpn_conv1\"\r\n  top: \"rpn_cls_score\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  convolution_param {\r\n    num_output: 18\r\n    pad: 0\r\n    kernel_size: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"gaussian\"\r\n      std: 0.01\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"rpn_bbox_pred\"\r\n  type: \"Convolution\"\r\n  bottom: \"rpn_conv1\"\r\n  top: \"rpn_bbox_pred\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  convolution_param {\r\n    num_output: 36\r\n    pad: 0\r\n    kernel_size: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"gaussian\"\r\n      std: 0.01\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"rpn_cls_score_reshape\"\r\n  type: \"Reshape\"\r\n  bottom: \"rpn_cls_score\"\r\n  top: \"rpn_cls_score_reshape\"\r\n  reshape_param {\r\n    shape {\r\n      dim: 0\r\n      dim: 2\r\n      dim: -1\r\n      dim: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"rpn-data\"\r\n  type: \"Python\"\r\n  bottom: \"rpn_cls_score\"\r\n  bottom: \"gt_boxes\"\r\n  bottom: \"im_info\"\r\n  bottom: \"data\"\r\n  top: \"rpn_labels\"\r\n  top: \"rpn_bbox_targets\"\r\n  top: \"rpn_bbox_inside_weights\"\r\n  top: \"rpn_bbox_outside_weights\"\r\n  python_param {\r\n    module: \"rpn.anchor_target_layer\"\r\n    layer: \"AnchorTargetLayer\"\r\n    param_str: \"\\'feat_stride\\': 16\"\r\n  }\r\n}\r\nlayer {\r\n  name: \"rpn_loss_cls\"\r\n  type: \"SoftmaxWithLoss\"\r\n  bottom: \"rpn_cls_score_reshape\"\r\n  bottom: \"rpn_labels\"\r\n  top: \"rpn_cls_loss\"\r\n  loss_weight: 1\r\n  propagate_down: true\r\n  propagate_down: false\r\n  loss_param {\r\n    ignore_label: -1\r\n    normalize: true\r\n  }\r\n}\r\nlayer {\r\n  name: \"rpn_loss_bbox\"\r\n  type: \"SmoothL1Loss\"\r\n  bottom: \"rpn_bbox_pred\"\r\n  bottom: \"rpn_bbox_targets\"\r\n  bottom: \"rpn_bbox_inside_weights\"\r\n  bottom: \"rpn_bbox_outside_weights\"\r\n  top: \"rpn_loss_bbox\"\r\n  loss_weight: 1\r\n  smooth_l1_loss_param {\r\n    sigma: 3\r\n  }\r\n}\r\nlayer {\r\n  name: \"dummy_roi_pool_conv5\"\r\n  type: \"DummyData\"\r\n  top: \"dummy_roi_pool_conv5\"\r\n  dummy_data_param {\r\n    data_filler {\r\n      type: \"gaussian\"\r\n      std: 0.01\r\n    }\r\n    shape {\r\n      dim: 1\r\n      dim: 9216\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"fc6\"\r\n  type: \"InnerProduct\"\r\n  bottom: \"dummy_roi_pool_conv5\"\r\n  top: \"fc6\"\r\n  param {\r\n    lr_mult: 0\r\n    decay_mult: 0\r\n  }\r\n  param {\r\n    lr_mult: 0\r\n    decay_mult: 0\r\n  }\r\n  inner_product_param {\r\n    num_output: 4096\r\n  }\r\n}\r\nlayer {\r\n  name: \"relu6\"\r\n  type: \"ReLU\"\r\n  bottom: \"fc6\"\r\n  top: \"fc6\"\r\n}\r\nlayer {\r\n  name: \"fc7\"\r\n  type: \"InnerProduct\"\r\n  bottom: \"fc6\"\r\n  top: \"fc7\"\r\n  param {\r\n    lr_mult: 0\r\n    decay_mult: 0\r\n  }\r\n  param {\r\n    lr_mult: 0\r\n    decay_mult: 0\r\n  }\r\n  inner_product_param {\r\n    num_output: 4096\r\n  }\r\n}\r\nlayer {\r\n  name: \"silence_fc7\"\r\n  type: \"Silence\"\r\n  bottom: \"fc7\"\r\n}\r\nI0926 21:27:25.823921 3110372288 layer_factory.hpp:77] Creating layer input-data\r\nI0926 21:27:25.825009 3110372288 net.cpp:106] Creating Layer input-data\r\nI0926 21:27:25.825080 3110372288 net.cpp:411] input-data -> data\r\nI0926 21:27:25.825131 3110372288 net.cpp:411] input-data -> im_info\r\nI0926 21:27:25.825165 3110372288 net.cpp:411] input-data -> gt_boxes\r\nRoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1}\r\nI0926 21:27:25.826084 3110372288 net.cpp:150] Setting up input-data\r\nI0926 21:27:25.826138 3110372288 net.cpp:157] Top shape: 1 3 224 224 (150528)\r\nI0926 21:27:25.826182 3110372288 net.cpp:157] Top shape: 1 3 (3)\r\nI0926 21:27:25.826223 3110372288 net.cpp:157] Top shape: 1 4 (4)\r\nI0926 21:27:25.826261 3110372288 net.cpp:165] Memory required for data: 602140\r\nI0926 21:27:25.826298 3110372288 layer_factory.hpp:77] Creating layer data_input-data_0_split\r\nI0926 21:27:25.826341 3110372288 net.cpp:106] Creating Layer data_input-data_0_split\r\nI0926 21:27:25.826377 3110372288 net.cpp:454] data_input-data_0_split <- data\r\nI0926 21:27:25.826411 3110372288 net.cpp:411] data_input-data_0_split -> data_input-data_0_split_0\r\nI0926 21:27:25.826449 3110372288 net.cpp:411] data_input-data_0_split -> data_input-data_0_split_1\r\nI0926 21:27:25.826495 3110372288 net.cpp:150] Setting up data_input-data_0_split\r\nI0926 21:27:25.826532 3110372288 net.cpp:157] Top shape: 1 3 224 224 (150528)\r\nI0926 21:27:25.826565 3110372288 net.cpp:157] Top shape: 1 3 224 224 (150528)\r\nI0926 21:27:25.826601 3110372288 net.cpp:165] Memory required for data: 1806364\r\nI0926 21:27:25.826634 3110372288 layer_factory.hpp:77] Creating layer conv1\r\nI0926 21:27:25.826674 3110372288 net.cpp:106] Creating Layer conv1\r\nI0926 21:27:25.826709 3110372288 net.cpp:454] conv1 <- data_input-data_0_split_0\r\nI0926 21:27:25.826745 3110372288 net.cpp:411] conv1 -> conv1\r\nI0926 21:27:25.826910 3110372288 net.cpp:150] Setting up conv1\r\nI0926 21:27:25.826939 3110372288 net.cpp:157] Top shape: 1 96 112 112 (1204224)\r\nI0926 21:27:25.826968 3110372288 net.cpp:165] Memory required for data: 6623260\r\nI0926 21:27:25.827008 3110372288 layer_factory.hpp:77] Creating layer relu1\r\nI0926 21:27:25.827049 3110372288 net.cpp:106] Creating Layer relu1\r\nI0926 21:27:25.827085 3110372288 net.cpp:454] relu1 <- conv1\r\nI0926 21:27:25.827121 3110372288 net.cpp:397] relu1 -> conv1 (in-place)\r\nI0926 21:27:25.827184 3110372288 net.cpp:150] Setting up relu1\r\nI0926 21:27:25.827236 3110372288 net.cpp:157] Top shape: 1 96 112 112 (1204224)\r\nI0926 21:27:25.827329 3110372288 net.cpp:165] Memory required for data: 11440156\r\nI0926 21:27:25.827364 3110372288 layer_factory.hpp:77] Creating layer norm1\r\nI0926 21:27:25.827401 3110372288 net.cpp:106] Creating Layer norm1\r\nI0926 21:27:25.827545 3110372288 net.cpp:454] norm1 <- conv1\r\nI0926 21:27:25.827579 3110372288 net.cpp:411] norm1 -> norm1\r\nI0926 21:27:25.827627 3110372288 net.cpp:150] Setting up norm1\r\nI0926 21:27:25.827652 3110372288 net.cpp:157] Top shape: 1 96 112 112 (1204224)\r\nI0926 21:27:25.827684 3110372288 net.cpp:165] Memory required for data: 16257052\r\nI0926 21:27:25.827718 3110372288 layer_factory.hpp:77] Creating layer pool1\r\nI0926 21:27:25.827764 3110372288 net.cpp:106] Creating Layer pool1\r\nI0926 21:27:25.827800 3110372288 net.cpp:454] pool1 <- norm1\r\nI0926 21:27:25.827826 3110372288 net.cpp:411] pool1 -> pool1\r\nI0926 21:27:25.827858 3110372288 net.cpp:150] Setting up pool1\r\nI0926 21:27:25.827888 3110372288 net.cpp:157] Top shape: 1 96 57 57 (311904)\r\nI0926 21:27:25.827915 3110372288 net.cpp:165] Memory required for data: 17504668\r\nI0926 21:27:25.827971 3110372288 layer_factory.hpp:77] Creating layer conv2\r\nI0926 21:27:25.828006 3110372288 net.cpp:106] Creating Layer conv2\r\nI0926 21:27:25.828054 3110372288 net.cpp:454] conv2 <- pool1\r\nI0926 21:27:25.828186 3110372288 net.cpp:411] conv2 -> conv2\r\nI0926 21:27:25.829418 3110372288 net.cpp:150] Setting up conv2\r\nI0926 21:27:25.829468 3110372288 net.cpp:157] Top shape: 1 256 29 29 (215296)\r\nI0926 21:27:25.829493 3110372288 net.cpp:165] Memory required for data: 18365852\r\nI0926 21:27:25.829516 3110372288 layer_factory.hpp:77] Creating layer relu2\r\nI0926 21:27:25.829540 3110372288 net.cpp:106] Creating Layer relu2\r\nI0926 21:27:25.829562 3110372288 net.cpp:454] relu2 <- conv2\r\nI0926 21:27:25.829622 3110372288 net.cpp:397] relu2 -> conv2 (in-place)\r\nI0926 21:27:25.829655 3110372288 net.cpp:150] Setting up relu2\r\nI0926 21:27:25.829685 3110372288 net.cpp:157] Top shape: 1 256 29 29 (215296)\r\nI0926 21:27:25.829733 3110372288 net.cpp:165] Memory required for data: 19227036\r\nI0926 21:27:25.829774 3110372288 layer_factory.hpp:77] Creating layer norm2\r\nI0926 21:27:25.829823 3110372288 net.cpp:106] Creating Layer norm2\r\nI0926 21:27:25.829850 3110372288 net.cpp:454] norm2 <- conv2\r\nI0926 21:27:25.829901 3110372288 net.cpp:411] norm2 -> norm2\r\nI0926 21:27:25.829960 3110372288 net.cpp:150] Setting up norm2\r\nI0926 21:27:25.829988 3110372288 net.cpp:157] Top shape: 1 256 29 29 (215296)\r\nI0926 21:27:25.830015 3110372288 net.cpp:165] Memory required for data: 20088220\r\nI0926 21:27:25.830056 3110372288 layer_factory.hpp:77] Creating layer pool2\r\nI0926 21:27:25.830091 3110372288 net.cpp:106] Creating Layer pool2\r\nI0926 21:27:25.830140 3110372288 net.cpp:454] pool2 <- norm2\r\nI0926 21:27:25.830169 3110372288 net.cpp:411] pool2 -> pool2\r\nI0926 21:27:25.830217 3110372288 net.cpp:150] Setting up pool2\r\nI0926 21:27:25.830262 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)\r\nI0926 21:27:25.830292 3110372288 net.cpp:165] Memory required for data: 20318620\r\nI0926 21:27:25.830330 3110372288 layer_factory.hpp:77] Creating layer conv3\r\nI0926 21:27:25.830381 3110372288 net.cpp:106] Creating Layer conv3\r\nI0926 21:27:25.830430 3110372288 net.cpp:454] conv3 <- pool2\r\nI0926 21:27:25.830461 3110372288 net.cpp:411] conv3 -> conv3\r\nI0926 21:27:25.832145 3110372288 net.cpp:150] Setting up conv3\r\nI0926 21:27:25.832204 3110372288 net.cpp:157] Top shape: 1 384 15 15 (86400)\r\nI0926 21:27:25.832227 3110372288 net.cpp:165] Memory required for data: 20664220\r\nI0926 21:27:25.832252 3110372288 layer_factory.hpp:77] Creating layer relu3\r\nI0926 21:27:25.832278 3110372288 net.cpp:106] Creating Layer relu3\r\nI0926 21:27:25.832298 3110372288 net.cpp:454] relu3 <- conv3\r\nI0926 21:27:25.832320 3110372288 net.cpp:397] relu3 -> conv3 (in-place)\r\nI0926 21:27:25.832383 3110372288 net.cpp:150] Setting up relu3\r\nI0926 21:27:25.832412 3110372288 net.cpp:157] Top shape: 1 384 15 15 (86400)\r\nI0926 21:27:25.832443 3110372288 net.cpp:165] Memory required for data: 21009820\r\nI0926 21:27:25.832499 3110372288 layer_factory.hpp:77] Creating layer conv4\r\nI0926 21:27:25.832537 3110372288 net.cpp:106] Creating Layer conv4\r\nI0926 21:27:25.832572 3110372288 net.cpp:454] conv4 <- conv3\r\nI0926 21:27:25.832600 3110372288 net.cpp:411] conv4 -> conv4\r\nI0926 21:27:25.835286 3110372288 net.cpp:150] Setting up conv4\r\nI0926 21:27:25.835361 3110372288 net.cpp:157] Top shape: 1 384 15 15 (86400)\r\nI0926 21:27:25.835384 3110372288 net.cpp:165] Memory required for data: 21355420\r\nI0926 21:27:25.835407 3110372288 layer_factory.hpp:77] Creating layer relu4\r\nI0926 21:27:25.835436 3110372288 net.cpp:106] Creating Layer relu4\r\nI0926 21:27:25.835466 3110372288 net.cpp:454] relu4 <- conv4\r\nI0926 21:27:25.835495 3110372288 net.cpp:397] relu4 -> conv4 (in-place)\r\nI0926 21:27:25.835573 3110372288 net.cpp:150] Setting up relu4\r\nI0926 21:27:25.835609 3110372288 net.cpp:157] Top shape: 1 384 15 15 (86400)\r\nI0926 21:27:25.835638 3110372288 net.cpp:165] Memory required for data: 21701020\r\nI0926 21:27:25.835666 3110372288 layer_factory.hpp:77] Creating layer conv5\r\nI0926 21:27:25.835716 3110372288 net.cpp:106] Creating Layer conv5\r\nI0926 21:27:25.835750 3110372288 net.cpp:454] conv5 <- conv4\r\nI0926 21:27:25.835794 3110372288 net.cpp:411] conv5 -> conv5\r\nI0926 21:27:25.837648 3110372288 net.cpp:150] Setting up conv5\r\nI0926 21:27:25.837716 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)\r\nI0926 21:27:25.837740 3110372288 net.cpp:165] Memory required for data: 21931420\r\nI0926 21:27:25.837766 3110372288 layer_factory.hpp:77] Creating layer relu5\r\nI0926 21:27:25.837792 3110372288 net.cpp:106] Creating Layer relu5\r\nI0926 21:27:25.837813 3110372288 net.cpp:454] relu5 <- conv5\r\nI0926 21:27:25.837836 3110372288 net.cpp:397] relu5 -> conv5 (in-place)\r\nI0926 21:27:25.837860 3110372288 net.cpp:150] Setting up relu5\r\nI0926 21:27:25.837887 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)\r\nI0926 21:27:25.837913 3110372288 net.cpp:165] Memory required for data: 22161820\r\nI0926 21:27:25.837939 3110372288 layer_factory.hpp:77] Creating layer rpn_conv1\r\nI0926 21:27:25.837987 3110372288 net.cpp:106] Creating Layer rpn_conv1\r\nI0926 21:27:25.838016 3110372288 net.cpp:454] rpn_conv1 <- conv5\r\nI0926 21:27:25.838050 3110372288 net.cpp:411] rpn_conv1 -> rpn_conv1\r\nI0926 21:27:25.844331 3110372288 net.cpp:150] Setting up rpn_conv1\r\nI0926 21:27:25.844410 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)\r\nI0926 21:27:25.844435 3110372288 net.cpp:165] Memory required for data: 22392220\r\nI0926 21:27:25.844460 3110372288 layer_factory.hpp:77] Creating layer rpn_relu1\r\nI0926 21:27:25.844485 3110372288 net.cpp:106] Creating Layer rpn_relu1\r\nI0926 21:27:25.844506 3110372288 net.cpp:454] rpn_relu1 <- rpn_conv1\r\nI0926 21:27:25.844528 3110372288 net.cpp:397] rpn_relu1 -> rpn_conv1 (in-place)\r\nI0926 21:27:25.844591 3110372288 net.cpp:150] Setting up rpn_relu1\r\nI0926 21:27:25.844637 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)\r\nI0926 21:27:25.844676 3110372288 net.cpp:165] Memory required for data: 22622620\r\nI0926 21:27:25.844707 3110372288 layer_factory.hpp:77] Creating layer rpn_conv1_rpn_relu1_0_split\r\nI0926 21:27:25.844739 3110372288 net.cpp:106] Creating Layer rpn_conv1_rpn_relu1_0_split\r\nI0926 21:27:25.844765 3110372288 net.cpp:454] rpn_conv1_rpn_relu1_0_split <- rpn_conv1\r\nI0926 21:27:25.844797 3110372288 net.cpp:411] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_0\r\nI0926 21:27:25.844842 3110372288 net.cpp:411] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_1\r\nI0926 21:27:25.844883 3110372288 net.cpp:150] Setting up rpn_conv1_rpn_relu1_0_split\r\nI0926 21:27:25.844962 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)\r\nI0926 21:27:25.845007 3110372288 net.cpp:157] Top shape: 1 256 15 15 (57600)\r\nI0926 21:27:25.845037 3110372288 net.cpp:165] Memory required for data: 23083420\r\nI0926 21:27:25.845065 3110372288 layer_factory.hpp:77] Creating layer rpn_cls_score\r\nI0926 21:27:25.845113 3110372288 net.cpp:106] Creating Layer rpn_cls_score\r\nI0926 21:27:25.845145 3110372288 net.cpp:454] rpn_cls_score <- rpn_conv1_rpn_relu1_0_split_0\r\nI0926 21:27:25.845185 3110372288 net.cpp:411] rpn_cls_score -> rpn_cls_score\r\nI0926 21:27:25.845288 3110372288 net.cpp:150] Setting up rpn_cls_score\r\nI0926 21:27:25.845337 3110372288 net.cpp:157] Top shape: 1 18 15 15 (4050)\r\nI0926 21:27:25.845377 3110372288 net.cpp:165] Memory required for data: 23099620\r\nI0926 21:27:25.845409 3110372288 layer_factory.hpp:77] Creating layer rpn_cls_score_rpn_cls_score_0_split\r\nI0926 21:27:25.845443 3110372288 net.cpp:106] Creating Layer rpn_cls_score_rpn_cls_score_0_split\r\nI0926 21:27:25.845492 3110372288 net.cpp:454] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score\r\nI0926 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\r\nI0926 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\r\nI0926 21:27:25.845623 3110372288 net.cpp:150] Setting up rpn_cls_score_rpn_cls_score_0_split\r\nI0926 21:27:25.845716 3110372288 net.cpp:157] Top shape: 1 18 15 15 (4050)\r\nI0926 21:27:25.845747 3110372288 net.cpp:157] Top shape: 1 18 15 15 (4050)\r\nI0926 21:27:25.845777 3110372288 net.cpp:165] Memory required for data: 23132020\r\nI0926 21:27:25.845806 3110372288 layer_factory.hpp:77] Creating layer rpn_bbox_pred\r\nI0926 21:27:25.845865 3110372288 net.cpp:106] Creating Layer rpn_bbox_pred\r\nI0926 21:27:25.845897 3110372288 net.cpp:454] rpn_bbox_pred <- rpn_conv1_rpn_relu1_0_split_1\r\nI0926 21:27:25.845929 3110372288 net.cpp:411] rpn_bbox_pred -> rpn_bbox_pred\r\nI0926 21:27:25.846082 3110372288 net.cpp:150] Setting up rpn_bbox_pred\r\nI0926 21:27:25.846112 3110372288 net.cpp:157] Top shape: 1 36 15 15 (8100)\r\nI0926 21:27:25.846134 3110372288 net.cpp:165] Memory required for data: 23164420\r\nI0926 21:27:25.846156 3110372288 layer_factory.hpp:77] Creating layer rpn_cls_score_reshape\r\nI0926 21:27:25.846249 3110372288 net.cpp:106] Creating Layer rpn_cls_score_reshape\r\nI0926 21:27:25.846290 3110372288 net.cpp:454] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0\r\nI0926 21:27:25.846326 3110372288 net.cpp:411] rpn_cls_score_reshape -> rpn_cls_score_reshape\r\nI0926 21:27:25.846385 3110372288 net.cpp:150] Setting up rpn_cls_score_reshape\r\nI0926 21:27:25.846417 3110372288 net.cpp:157] Top shape: 1 2 135 15 (4050)\r\nI0926 21:27:25.846446 3110372288 net.cpp:165] Memory required for data: 23180620\r\nI0926 21:27:25.846472 3110372288 layer_factory.hpp:77] Creating layer rpn-data\r\nI0926 21:27:25.846982 3110372288 net.cpp:106] Creating Layer rpn-data\r\nI0926 21:27:25.847013 3110372288 net.cpp:454] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1\r\nI0926 21:27:25.847035 3110372288 net.cpp:454] rpn-data <- gt_boxes\r\nI0926 21:27:25.847056 3110372288 net.cpp:454] rpn-data <- im_info\r\nI0926 21:27:25.847149 3110372288 net.cpp:454] rpn-data <- data_input-data_0_split_1\r\nI0926 21:27:25.847187 3110372288 net.cpp:411] rpn-data -> rpn_labels\r\nI0926 21:27:25.847240 3110372288 net.cpp:411] rpn-data -> rpn_bbox_targets\r\nI0926 21:27:25.847286 3110372288 net.cpp:411] rpn-data -> rpn_bbox_inside_weights\r\nI0926 21:27:25.847323 3110372288 net.cpp:411] rpn-data -> rpn_bbox_outside_weights\r\nI0926 21:27:25.848160 3110372288 net.cpp:150] Setting up rpn-data\r\nI0926 21:27:25.848197 3110372288 net.cpp:157] Top shape: 1 1 135 15 (2025)\r\nI0926 21:27:25.848222 3110372288 net.cpp:157] Top shape: 1 36 15 15 (8100)\r\nI0926 21:27:25.848254 3110372288 net.cpp:157] Top shape: 1 36 15 15 (8100)\r\nI0926 21:27:25.848307 3110372288 net.cpp:157] Top shape: 1 36 15 15 (8100)\r\nI0926 21:27:25.848340 3110372288 net.cpp:165] Memory required for data: 23285920\r\nI0926 21:27:25.848368 3110372288 layer_factory.hpp:77] Creating layer rpn_loss_cls\r\nI0926 21:27:25.848398 3110372288 net.cpp:106] Creating Layer rpn_loss_cls\r\nI0926 21:27:25.848424 3110372288 net.cpp:454] rpn_loss_cls <- rpn_cls_score_reshape\r\nI0926 21:27:25.848451 3110372288 net.cpp:454] rpn_loss_cls <- rpn_labels\r\nI0926 21:27:25.848479 3110372288 net.cpp:411] rpn_loss_cls -> rpn_cls_loss\r\nI0926 21:27:25.848527 3110372288 layer_factory.hpp:77] Creating layer rpn_loss_cls\r\nI0926 21:27:25.848604 3110372288 net.cpp:150] Setting up rpn_loss_cls\r\nI0926 21:27:25.848644 3110372288 net.cpp:157] Top shape: (1)\r\nI0926 21:27:25.848688 3110372288 net.cpp:160]     with loss weight 1\r\nI0926 21:27:25.848745 3110372288 net.cpp:165] Memory required for data: 23285924\r\nI0926 21:27:25.848793 3110372288 layer_factory.hpp:77] Creating layer rpn_loss_bbox\r\nI0926 21:27:25.848842 3110372288 net.cpp:106] Creating Layer rpn_loss_bbox\r\nI0926 21:27:25.848906 3110372288 net.cpp:454] rpn_loss_bbox <- rpn_bbox_pred\r\nI0926 21:27:25.848963 3110372288 net.cpp:454] rpn_loss_bbox <- rpn_bbox_targets\r\nI0926 21:27:25.849027 3110372288 net.cpp:454] rpn_loss_bbox <- rpn_bbox_inside_weights\r\nI0926 21:27:25.849084 3110372288 net.cpp:454] rpn_loss_bbox <- rpn_bbox_outside_weights\r\nI0926 21:27:25.849131 3110372288 net.cpp:411] rpn_loss_bbox -> rpn_loss_bbox\r\nI0926 21:27:25.849236 3110372288 net.cpp:150] Setting up rpn_loss_bbox\r\nI0926 21:27:25.849272 3110372288 net.cpp:157] Top shape: (1)\r\nI0926 21:27:25.849308 3110372288 net.cpp:160]     with loss weight 1\r\nI0926 21:27:25.849351 3110372288 net.cpp:165] Memory required for data: 23285928\r\nI0926 21:27:25.849457 3110372288 layer_factory.hpp:77] Creating layer dummy_roi_pool_conv5\r\nI0926 21:27:25.849503 3110372288 net.cpp:106] Creating Layer dummy_roi_pool_conv5\r\nI0926 21:27:25.849531 3110372288 net.cpp:411] dummy_roi_pool_conv5 -> dummy_roi_pool_conv5\r\nI0926 21:27:25.849573 3110372288 net.cpp:150] Setting up dummy_roi_pool_conv5\r\nI0926 21:27:25.849656 3110372288 net.cpp:157] Top shape: 1 9216 (9216)\r\nI0926 21:27:25.849700 3110372288 net.cpp:165] Memory required for data: 23322792\r\nI0926 21:27:25.849730 3110372288 layer_factory.hpp:77] Creating layer fc6\r\nI0926 21:27:25.849768 3110372288 net.cpp:106] Creating Layer fc6\r\nI0926 21:27:25.849862 3110372288 net.cpp:454] fc6 <- dummy_roi_pool_conv5\r\nI0926 21:27:25.849958 3110372288 net.cpp:411] fc6 -> fc6\r\nI0926 21:27:25.929716 3110372288 net.cpp:150] Setting up fc6\r\nI0926 21:27:25.929802 3110372288 net.cpp:157] Top shape: 1 4096 (4096)\r\nI0926 21:27:25.929832 3110372288 net.cpp:165] Memory required for data: 23339176\r\nI0926 21:27:25.929874 3110372288 layer_factory.hpp:77] Creating layer relu6\r\nI0926 21:27:25.929929 3110372288 net.cpp:106] Creating Layer relu6\r\nI0926 21:27:25.930035 3110372288 net.cpp:454] relu6 <- fc6\r\nI0926 21:27:25.930088 3110372288 net.cpp:397] relu6 -> fc6 (in-place)\r\nI0926 21:27:25.930126 3110372288 net.cpp:150] Setting up relu6\r\nI0926 21:27:25.930156 3110372288 net.cpp:157] Top shape: 1 4096 (4096)\r\nI0926 21:27:25.930182 3110372288 net.cpp:165] Memory required for data: 23355560\r\nI0926 21:27:25.930212 3110372288 layer_factory.hpp:77] Creating layer fc7\r\nI0926 21:27:25.930244 3110372288 net.cpp:106] Creating Layer fc7\r\nI0926 21:27:25.930274 3110372288 net.cpp:454] fc7 <- fc6\r\nI0926 21:27:25.930310 3110372288 net.cpp:411] fc7 -> fc7\r\nI0926 21:27:25.962868 3110372288 net.cpp:150] Setting up fc7\r\nI0926 21:27:25.962956 3110372288 net.cpp:157] Top shape: 1 4096 (4096)\r\nI0926 21:27:25.962991 3110372288 net.cpp:165] Memory required for data: 23371944\r\nI0926 21:27:25.963037 3110372288 layer_factory.hpp:77] Creating layer silence_fc7\r\nI0926 21:27:25.963112 3110372288 net.cpp:106] Creating Layer silence_fc7\r\nI0926 21:27:25.963172 3110372288 net.cpp:454] silence_fc7 <- fc7\r\nI0926 21:27:25.963210 3110372288 net.cpp:150] Setting up silence_fc7\r\nI0926 21:27:25.963241 3110372288 net.cpp:165] Memory required for data: 23371944\r\nI0926 21:27:25.963269 3110372288 net.cpp:228] silence_fc7 does not need backward computation.\r\nI0926 21:27:25.963315 3110372288 net.cpp:228] fc7 does not need backward computation.\r\nI0926 21:27:25.963348 3110372288 net.cpp:228] relu6 does not need backward computation.\r\nI0926 21:27:25.963410 3110372288 net.cpp:228] fc6 does not need backward computation.\r\nI0926 21:27:25.963455 3110372288 net.cpp:228] dummy_roi_pool_conv5 does not need backward computation.\r\nI0926 21:27:25.963598 3110372288 net.cpp:226] rpn_loss_bbox needs backward computation.\r\nI0926 21:27:25.963675 3110372288 net.cpp:226] rpn_loss_cls needs backward computation.\r\nI0926 21:27:25.963714 3110372288 net.cpp:226] rpn-data needs backward computation.\r\nI0926 21:27:25.963755 3110372288 net.cpp:226] rpn_cls_score_reshape needs backward computation.\r\nI0926 21:27:25.963796 3110372288 net.cpp:226] rpn_bbox_pred needs backward computation.\r\nI0926 21:27:25.963834 3110372288 net.cpp:226] rpn_cls_score_rpn_cls_score_0_split needs backward computation.\r\nI0926 21:27:25.963874 3110372288 net.cpp:226] rpn_cls_score needs backward computation.\r\nI0926 21:27:25.963910 3110372288 net.cpp:226] rpn_conv1_rpn_relu1_0_split needs backward computation.\r\nI0926 21:27:25.963944 3110372288 net.cpp:226] rpn_relu1 needs backward computation.\r\nI0926 21:27:25.963979 3110372288 net.cpp:226] rpn_conv1 needs backward computation.\r\nI0926 21:27:25.964015 3110372288 net.cpp:226] relu5 needs backward computation.\r\nI0926 21:27:25.964058 3110372288 net.cpp:226] conv5 needs backward computation.\r\nI0926 21:27:25.964095 3110372288 net.cpp:226] relu4 needs backward computation.\r\nI0926 21:27:25.964129 3110372288 net.cpp:226] conv4 needs backward computation.\r\nI0926 21:27:25.964193 3110372288 net.cpp:226] relu3 needs backward computation.\r\nI0926 21:27:25.964223 3110372288 net.cpp:226] conv3 needs backward computation.\r\nI0926 21:27:25.964253 3110372288 net.cpp:226] pool2 needs backward computation.\r\nI0926 21:27:25.964280 3110372288 net.cpp:226] norm2 needs backward computation.\r\nI0926 21:27:25.964306 3110372288 net.cpp:226] relu2 needs backward computation.\r\nI0926 21:27:25.964332 3110372288 net.cpp:226] conv2 needs backward computation.\r\nI0926 21:27:25.964359 3110372288 net.cpp:226] pool1 needs backward computation.\r\nI0926 21:27:25.964385 3110372288 net.cpp:226] norm1 needs backward computation.\r\nI0926 21:27:25.964409 3110372288 net.cpp:226] relu1 needs backward computation.\r\nI0926 21:27:25.964434 3110372288 net.cpp:226] conv1 needs backward computation.\r\nI0926 21:27:25.964463 3110372288 net.cpp:228] data_input-data_0_split does not need backward computation.\r\nI0926 21:27:25.964489 3110372288 net.cpp:228] input-data does not need backward computation.\r\nI0926 21:27:25.964514 3110372288 net.cpp:270] This network produces output rpn_cls_loss\r\nI0926 21:27:25.964540 3110372288 net.cpp:270] This network produces output rpn_loss_bbox\r\nI0926 21:27:25.964587 3110372288 net.cpp:283] Network initialization done.\r\nI0926 21:27:25.964679 3110372288 solver.cpp:60] Solver scaffolding done.\r\nLoading pretrained model weights from data/imagenet_models/ZF.v2.caffemodel\r\nI0926 21:27:26.130048 3110372288 net.cpp:816] Ignoring source layer data\r\nI0926 21:27:26.131903 3110372288 net.cpp:816] Ignoring source layer conv5_relu5_0_split\r\nI0926 21:27:26.131945 3110372288 net.cpp:816] Ignoring source layer roi_pool_conv5\r\nI0926 21:27:26.150430 3110372288 net.cpp:816] Ignoring source layer drop6\r\nI0926 21:27:26.159194 3110372288 net.cpp:816] Ignoring source layer relu7\r\nI0926 21:27:26.159286 3110372288 net.cpp:816] Ignoring source layer drop7\r\nI0926 21:27:26.159312 3110372288 net.cpp:816] Ignoring source layer fc7_drop7_0_split\r\nI0926 21:27:26.159335 3110372288 net.cpp:816] Ignoring source layer cls_score\r\nI0926 21:27:26.159399 3110372288 net.cpp:816] Ignoring source layer bbox_pred\r\nI0926 21:27:26.159451 3110372288 net.cpp:816] Ignoring source layer loss_cls\r\nI0926 21:27:26.159482 3110372288 net.cpp:816] Ignoring source layer loss_bbox\r\nI0926 21:27:26.159852 3110372288 net.cpp:816] Ignoring source layer silence_rpn_cls_score\r\nI0926 21:27:26.159898 3110372288 net.cpp:816] Ignoring source layer silence_rpn_bbox_pred\r\nSolving...\r\n```\r\npython error\r\n```\r\nProcess:               Python [21288]\r\nPath:                  /usr/local/Cellar/python/2.7.13_1/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python\r\nIdentifier:            Python\r\nVersion:               2.7.13 (2.7.13)\r\nCode Type:             X86-64 (Native)\r\nParent Process:        Python [21256]\r\nResponsible:           Python [21288]\r\nUser ID:               501\r\n\r\nDate/Time:             2017-09-26 21:49:13.131 +0800\r\nOS Version:            Mac OS X 10.12.6 (16G29)\r\nReport Version:        12\r\nAnonymous UUID:        178DEF6A-1A08-79ED-BBCB-131A4E4E6DFD\r\n\r\nSleep/Wake UUID:       B9C8F5F1-A14F-4403-9211-B7D9495EC662\r\n\r\nTime Awake Since Boot: 88000 seconds\r\nTime Since Wake:       5500 seconds\r\n\r\nSystem Integrity Protection: enabled\r\n\r\nCrashed Thread:        0  Dispatch queue: com.apple.main-thread\r\n\r\nException Type:        EXC_BAD_ACCESS (SIGSEGV)\r\nException Codes:       KERN_INVALID_ADDRESS at 0x0000000000000110\r\nException Note:        EXC_CORPSE_NOTIFY\r\n\r\nTermination Signal:    Segmentation fault: 11\r\nTermination Reason:    Namespace SIGNAL, Code 0xb\r\nTerminating Process:   exc handler [0]\r\n\r\nVM Regions Near 0x110:\r\n--> \r\n    __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\r\n\r\nApplication Specific Information:\r\n*** multi-threaded process forked ***\r\ncrashed on child side of fork pre-exec\r\n\r\nThread 0 Crashed:: Dispatch queue: com.apple.main-thread\r\n0   libdispatch.dylib             \t0x00007fffb0715041 _dispatch_root_queue_push_override + 204\r\n1   libxpc.dylib                  \t0x00007fffb09927ae _xpc_connection_activate_if_needed + 227\r\n2   libxpc.dylib                  \t0x00007fffb09855b2 xpc_connection_resume + 64\r\n3   libCoreVMClient.dylib         \t0x00007fff9f3faa1c cvms_connection_create + 216\r\n4   libCVMSPluginSupport.dylib    \t0x00007fff9f3f4533 cvmPreInit + 554\r\n5   libGFXShared.dylib            \t0x00007fff9f4015b1 gfxInitializeLibrary + 1802\r\n6   com.apple.opencl              \t0x00007fff9f39e727 0x7fff9f37e000 + 132903\r\n7   libsystem_pthread.dylib       \t0x00007fffb094db8c __pthread_once_handler + 65\r\n8   libsystem_platform.dylib      \t0x00007fffb0942ac1 _os_once + 36\r\n9   libsystem_pthread.dylib       \t0x00007fffb094db2b pthread_once + 57\r\n10  com.apple.opencl              \t0x00007fff9f39e4da 0x7fff9f37e000 + 132314\r\n11  com.apple.opencl              \t0x00007fff9f39eb66 clGetDeviceIDs + 182\r\n12  libopencv_core.3.3.dylib      \t0x000000010f0eae62 cv::ocl::Context::Impl::setDefault() + 3730\r\n13  libopencv_core.3.3.dylib      \t0x000000010f0e9cc0 cv::ocl::Context::getDefault(bool) + 156\r\n14  libopencv_core.3.3.dylib      \t0x000000010f0e7cf8 cv::ocl::Device::getDefault() + 16\r\n15  libopencv_core.3.3.dylib      \t0x000000010f0e7c92 cv::ocl::useOpenCL() + 43\r\n16  libopencv_imgproc.3.3.dylib   \t0x0000000110248fb5 cv::resize(cv::_InputArray const&, cv::_OutputArray const&, cv::Size_<int>, double, double, int) + 730\r\n17  cv2.so                        \t0x0000000124fb1b95 pyopencv_cv_resize(_object*, _object*, _object*) + 679\r\n18  org.python.python             \t0x000000010d327c8e PyEval_EvalFrameEx + 11464\r\n19  org.python.python             \t0x000000010d32db84 fast_function + 196\r\n20  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n21  org.python.python             \t0x000000010d32db84 fast_function + 196\r\n22  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n23  org.python.python             \t0x000000010d32db84 fast_function + 196\r\n24  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n25  org.python.python             \t0x000000010d32db84 fast_function + 196\r\n26  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n27  org.python.python             \t0x000000010d324de5 PyEval_EvalCodeEx + 1579\r\n28  org.python.python             \t0x000000010d2cb651 function_call + 330\r\n29  org.python.python             \t0x000000010d2adcda PyObject_Call + 97\r\n30  org.python.python             \t0x000000010d2b84d5 instancemethod_call + 163\r\n31  org.python.python             \t0x000000010d2adcda PyObject_Call + 97\r\n32  org.python.python             \t0x000000010d32d528 PyEval_CallObjectWithKeywords + 159\r\n33  org.python.python             \t0x000000010d341be6 PyEval_CallFunction + 170\r\n34  _caffe.so                     \t0x000000010e7eea60 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\r\n35  _caffe.so                     \t0x000000010e7ee8e5 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\r\n36  _caffe.so                     \t0x000000010e7edb0b 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\r\n37  libcaffe.so.1.0.0-rc3         \t0x000000010e9b9dde 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\r\n38  libcaffe.so.1.0.0-rc3         \t0x000000010ea3cbe1 caffe::Net<float>::ForwardFromTo(int, int) + 257\r\n39  libcaffe.so.1.0.0-rc3         \t0x000000010ea3d278 caffe::Net<float>::Forward(std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&, float*) + 120\r\n40  libcaffe.so.1.0.0-rc3         \t0x000000010ea5302e caffe::Solver<float>::Step(int) + 558\r\n41  _caffe.so                     \t0x000000010e7f2146 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\r\n42  libboost_python.dylib         \t0x0000000114081346 boost::python::objects::function::call(_object*, _object*) const + 822\r\n43  libboost_python.dylib         \t0x0000000114083eba boost::detail::function::void_function_ref_invoker0<boost::python::objects::(anonymous namespace)::bind_return, void>::invoke(boost::detail::function::function_buffer&) + 26\r\n44  libboost_python.dylib         \t0x0000000114089d8e boost::python::handle_exception_impl(boost::function0<void>) + 78\r\n45  libboost_python.dylib         \t0x00000001140839f3 boost::python::objects::function_call(_object*, _object*, _object*) + 83\r\n46  org.python.python             \t0x000000010d2adcda PyObject_Call + 97\r\n47  org.python.python             \t0x000000010d327b96 PyEval_EvalFrameEx + 11216\r\n48  org.python.python             \t0x000000010d32db84 fast_function + 196\r\n49  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n50  org.python.python             \t0x000000010d324de5 PyEval_EvalCodeEx + 1579\r\n51  org.python.python             \t0x000000010d32dbe2 fast_function + 290\r\n52  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n53  org.python.python             \t0x000000010d324de5 PyEval_EvalCodeEx + 1579\r\n54  org.python.python             \t0x000000010d2cb651 function_call + 330\r\n55  org.python.python             \t0x000000010d2adcda PyObject_Call + 97\r\n56  org.python.python             \t0x000000010d32a12f PyEval_EvalFrameEx + 20841\r\n57  org.python.python             \t0x000000010d32db84 fast_function + 196\r\n58  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n59  org.python.python             \t0x000000010d32db84 fast_function + 196\r\n60  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n61  org.python.python             \t0x000000010d324de5 PyEval_EvalCodeEx + 1579\r\n62  org.python.python             \t0x000000010d2cb651 function_call + 330\r\n63  org.python.python             \t0x000000010d2adcda PyObject_Call + 97\r\n64  org.python.python             \t0x000000010d2b84d5 instancemethod_call + 163\r\n65  org.python.python             \t0x000000010d2adcda PyObject_Call + 97\r\n66  org.python.python             \t0x000000010d2f3df7 slot_tp_init + 64\r\n67  org.python.python             \t0x000000010d2f0fff type_call + 182\r\n68  org.python.python             \t0x000000010d2adcda PyObject_Call + 97\r\n69  org.python.python             \t0x000000010d327b96 PyEval_EvalFrameEx + 11216\r\n70  org.python.python             \t0x000000010d32db84 fast_function + 196\r\n71  org.python.python             \t0x000000010d327ae8 PyEval_EvalFrameEx + 11042\r\n72  org.python.python             \t0x000000010d324de5 PyEval_EvalCodeEx + 1579\r\n73  org.python.python             \t0x000000010d3247b4 PyEval_EvalCode + 32\r\n74  org.python.python             \t0x000000010d345fdf run_mod + 49\r\n75  org.python.python             \t0x000000010d346086 PyRun_FileExFlags + 130\r\n76  org.python.python             \t0x000000010d345c03 PyRun_SimpleFileExFlags + 697\r\n77  org.python.python             \t0x000000010d356ee5 Py_Main + 3013\r\n78  libdyld.dylib                 \t0x00007fffb0734235 start + 1\r\n\r\nThread 0 crashed with X86 Thread State (64-bit):\r\n  rax: 0x00000001208018c0  rbx: 0x0000000000000480  rcx: 0x0000000000000100  rdx: 0x0000000000000001\r\n  rdi: 0x0000000120800000  rsi: 0x0000000120800080  rbp: 0x00007fff5295a7e0  rsp: 0x00007fff5295a7c0\r\n   r8: 0x00000001208018c0   r9: 0x0000000120800000  r10: 0x0000000100000000  r11: 0x0000000c00000000\r\n  r12: 0x00007fffb9617e40  r13: 0x00007fff5295ad40  r14: 0x00007fffb96181c0  r15: 0x0000000120801880\r\n  rip: 0x00007fffb0715041  rfl: 0x0000000000010206  cr2: 0x0000000000000110\r\n  \r\nLogical CPU:     1\r\nError Code:      0x00000006\r\nTrap Number:     14\r\n\r\n\r\nBinary Images:\r\n       0x10d29f000 -        0x10d2a0fff +org.python.python (2.7.13 - 2.7.13) <71AE39AF-B36C-3A2A-A241-A86A1CB5698F> /usr/local/Cellar/python/2.7.13_1/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python\r\n       0x10d2a3000 -        0x10d396ff7 +org.python.python (2.7.13, [c] 2001-2016 Python Software Foundation. - 2.7.13) <51E6D1FA-DCF4-3E2A-A3AE-368E9DA87153> /usr/local/Cellar/python/2.7.13_1/Frameworks/Python.framework/Versions/2.7/Python\r\n       0x10d45d000 -        0x10d46fff3  libTraditionalChineseConverter.dylib (69) <62B80E34-CB09-360D-9CD0-959C59756869> /System/Library/CoreServices/Encodings/libTraditionalChineseConverter.dylib\r\n      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/Users/USER/Library/Python/2.7/lib/python/site-packages/skimage/transform/_warps_cy.so\r\n       0x124a22000 -        0x124a3fff3 +_radon_transform.so (0) <BD1AF77C-6A9A-3955-9DB1-61D510641B7C> /Users/USER/Library/Python/2.7/lib/python/site-packages/skimage/transform/_radon_transform.so\r\n       0x124a4f000 -        0x124a71fff +_seam_carving.so (0) <304A1C9E-0AB3-32E8-8B47-3F8EF408250E> /Users/USER/Library/Python/2.7/lib/python/site-packages/skimage/transform/_seam_carving.so\r\n       0x124d02000 -        0x124d0bffb +cython_bbox.so (0) <A8CC3D98-C214-3285-80F7-9779BF04BE0E> /Users/USER/*/cython_bbox.so\r\n       0x124d11000 -        0x124d16fff +mio_utils.so (???) <B0A0ED1C-EB32-3AD4-BFCF-70AB62E194BF> /Users/USER/Library/Python/2.7/lib/python/site-packages/scipy/io/matlab/mio_utils.so\r\n       0x124d1c000 -        0x124d40fff +mio5_utils.so (???) <DB44C179-D55D-330D-9249-C4A2A8DC00AE> /Users/USER/Library/Python/2.7/lib/python/site-packages/scipy/io/matlab/mio5_utils.so\r\n       0x124d52000 -        0x124d60fff +streams.so (???) <E8401BB8-D28A-311E-A702-C4F2A06B1A5D> /Users/USER/Library/Python/2.7/lib/python/site-packages/scipy/io/matlab/streams.so\r\n       0x124dab000 -        0x124dadfff +mmap.so (0) <4F74BEE6-86DA-3F1D-92BE-FF2F650FAE9A> /usr/local/Cellar/python/2.7.13_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/mmap.so\r\n       0x124df1000 -        0x124e13ffb +_mask.so (0) <FB549DE0-5651-3777-9FED-0ED33BC0C309> /Users/USER/*/_mask.so\r\n       0x124e23000 -        0x125181ff3 +cv2.so (0) <7BC73FD3-F11C-38E6-9CBB-E62A125FD53B> /usr/local/lib/python2.7/site-packages/cv2.so\r\n       0x125255000 -        0x125270fff +libopencv_reg.3.3.dylib (0) <79CF369E-B9EF-3E81-A63C-32F9FCDD4706> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_reg.3.3.dylib\r\n       0x125277000 -        0x1252acfff +libopencv_surface_matching.3.3.dylib (0) <72A62C5E-5DF9-3B51-ABFF-74C8F16CE163> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_surface_matching.3.3.dylib\r\n       0x1252c8000 -        0x125344ff7 +libopencv_xphoto.3.3.dylib (0) <F7EFFE33-E730-393B-8AA2-51E444EA0A44> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_xphoto.3.3.dylib\r\n       0x12535d000 -        0x125368ff3 +libopencv_bgsegm.3.3.dylib (0) <AF91D43B-ED02-3505-9335-1678CBD6EB49> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_bgsegm.3.3.dylib\r\n       0x12536f000 -        0x12538bffb +libopencv_face.3.3.dylib (0) <1A86C528-7B41-378C-B573-0201C6078F48> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_face.3.3.dylib\r\n       0x125394000 -        0x1253adfff +libopencv_fuzzy.3.3.dylib (0) <0349D7F1-CE8D-343C-BE61-46F39CBB8F7D> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_fuzzy.3.3.dylib\r\n       0x1253b2000 -        0x1253bfff3 +libopencv_img_hash.3.3.dylib (0) <6C3F21B9-E135-378B-8639-6E118D23E646> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_img_hash.3.3.dylib\r\n       0x1253c8000 -        0x1253daffb +libopencv_xobjdetect.3.3.dylib (0) <3D24504A-EC67-307B-9672-4D30A59B533D> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_xobjdetect.3.3.dylib\r\n       0x1253e2000 -        0x1253faff7 +libopencv_superres.3.3.dylib (0) <549E4A49-1AB2-39D7-8497-B0A7AAB19EF5> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_superres.3.3.dylib\r\n       0x125408000 -        0x12543cfff +libopencv_bioinspired.3.3.dylib (0) <00CA1539-7710-3B43-851A-D182C1C509FB> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_bioinspired.3.3.dylib\r\n       0x125451000 -        0x125462ff7 +libopencv_dpm.3.3.dylib (0) <6EF41EEF-B015-3C3A-A4B1-AE0052DFE592> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_dpm.3.3.dylib\r\n       0x12546a000 -        0x12548bfff +libopencv_line_descriptor.3.3.dylib (0) <D372EFF4-9C92-37B5-99D3-2066E32F597C> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_line_descriptor.3.3.dylib\r\n       0x12549b000 -        0x1254bdff3 +libopencv_saliency.3.3.dylib (0) <E6A063FC-200E-343A-A4D7-F1404CA7BCD2> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_saliency.3.3.dylib\r\n       0x1254cf000 -        0x12555eff3 +libopencv_ccalib.3.3.dylib (0) <3EEE81DB-8178-34DD-B0EE-F07B43F2670C> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_ccalib.3.3.dylib\r\n       0x12556e000 -        0x1255e2ff7 +libopencv_rgbd.3.3.dylib (0) <775EF6DF-FAF8-3B0B-96ED-37EC58218F71> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_rgbd.3.3.dylib\r\n       0x1255fb000 -        0x12560cff7 +libopencv_structured_light.3.3.dylib (0) <32030E49-06C9-3A75-B6C5-5EB8581C769C> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_structured_light.3.3.dylib\r\n       0x125614000 -        0x125915ff3 +libopencv_tracking.3.3.dylib (0) <16B8D4B3-58F0-3A80-8846-4568037C1E90> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_tracking.3.3.dylib\r\n       0x125942000 -        0x125972ff7 +libopencv_videostab.3.3.dylib (0) <FB1D30B6-9697-389F-A806-30FC5A910E39> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_videostab.3.3.dylib\r\n       0x125989000 -        0x1259c1ffb +libopencv_aruco.3.3.dylib (0) <E784443F-8115-3F28-B681-4CB4A9651A54> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_aruco.3.3.dylib\r\n       0x1259e5000 -        0x125a32fdf +libopencv_optflow.3.3.dylib (0) <DD232C5B-3825-3290-BBC3-653BF0ACF55B> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_optflow.3.3.dylib\r\n       0x125a48000 -        0x125aabffb +libopencv_stitching.3.3.dylib (0) <638A1BF9-2D00-31C5-86EA-38706CC9B048> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_stitching.3.3.dylib\r\n       0x125ad0000 -        0x125b28feb +libopencv_objdetect.3.3.dylib (0) <C6FD1CDD-552A-3F2A-A478-16EABA2CF966> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_objdetect.3.3.dylib\r\n       0x125b43000 -        0x125b47fff +libopencv_phase_unwrapping.3.3.dylib (0) <20BBBDA8-8382-3FDA-BE72-AE4C21F21A99> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_phase_unwrapping.3.3.dylib\r\n       0x125b4d000 -        0x125b53ff3 +libopencv_plot.3.3.dylib (0) <AB982CD9-800D-3F12-BFAE-1C7F70D1DACB> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_plot.3.3.dylib\r\n       0x125b57000 -        0x125d27ff3 +libopencv_dnn.3.3.dylib (0) <C09F6F8A-32ED-37F6-9496-40B231237752> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_dnn.3.3.dylib\r\n       0x125e44000 -        0x125e79ff7 +libopencv_datasets.3.3.dylib (0) <E840054F-3FD1-346A-A3C0-B3DDEE1DD4A5> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_datasets.3.3.dylib\r\n       0x125e9d000 -        0x125efbffb +libopencv_text.3.3.dylib (0) <0B687C33-15AC-3710-9B94-49DE945AA198> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_text.3.3.dylib\r\n       0x125fd7000 -        0x126068ffb +libopencv_ml.3.3.dylib (0) <9794880F-E7CC-3AB5-B276-E5A584628D8B> /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_ml.3.3.dylib\r\n       0x126088000 -        0x126132ff7 +libopencv_photo.3.3.dylib (0) <44E3F12C-B2CB-3BAE-9E22-EF7DED697C71> 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libarchive.2.dylib (41.70.1) <A3FA7D6E-3D68-3524-8B48-EB2ECA44B13B> /usr/lib/libarchive.2.dylib\r\n    0x7fffaf223000 -     0x7fffaf29ffc7  libate.dylib (1.12.13) <D0767875-D02E-3377-84D8-5F174C27BEA9> /usr/lib/libate.dylib\r\n    0x7fffaf2a3000 -     0x7fffaf2a3ff3  libauto.dylib (187) <34388D0B-C539-3C1B-9408-2BC152162E43> /usr/lib/libauto.dylib\r\n    0x7fffaf2a4000 -     0x7fffaf2b4ff3  libbsm.0.dylib (34) <20084796-B04D-3B35-A003-EA11459557A9> /usr/lib/libbsm.0.dylib\r\n    0x7fffaf2b5000 -     0x7fffaf2c3ff7  libbz2.1.0.dylib (38) <ADFA329A-DCE7-356D-8F09-A3168DFC6610> /usr/lib/libbz2.1.0.dylib\r\n    0x7fffaf2c4000 -     0x7fffaf31aff7  libc++.1.dylib (307.5) <0B43BB5D-E6EB-3464-8DE9-B41AC8ED9D1C> /usr/lib/libc++.1.dylib\r\n    0x7fffaf31b000 -     0x7fffaf344ff7  libc++abi.dylib (307.4) <BC271AD3-831B-362A-9DA7-E8C51F285FE4> /usr/lib/libc++abi.dylib\r\n    0x7fffaf345000 -     0x7fffaf355ffb  libcmph.dylib (6) <2B5D405E-2D0B-3320-ABD6-622934C86ABE> /usr/lib/libcmph.dylib\r\n    0x7fffaf356000 -     0x7fffaf36cfcf  libcompression.dylib (39) <F2726F95-F54E-3B21-BCB5-F7151DEFDC2F> /usr/lib/libcompression.dylib\r\n    0x7fffaf36d000 -     0x7fffaf36dff7  libcoretls.dylib (121.50.4) <64B1001E-10F6-3542-A3B2-C4B49F51817F> /usr/lib/libcoretls.dylib\r\n    0x7fffaf36e000 -     0x7fffaf36fff3  libcoretls_cfhelpers.dylib (121.50.4) <1A10303E-5EB0-3C7C-9165-021FCDFD934D> /usr/lib/libcoretls_cfhelpers.dylib\r\n    0x7fffaf429000 -     0x7fffaf50eff7  libcrypto.0.9.8.dylib (64.50.6) <D34E16A7-990A-37A9-933A-DFAA46554EAA> /usr/lib/libcrypto.0.9.8.dylib\r\n    0x7fffaf6ac000 -     0x7fffaf6ffff7  libcups.2.dylib (450) <9950BFCB-7882-33C9-9ECF-CE66773C5657> /usr/lib/libcups.2.dylib\r\n    0x7fffaf753000 -     0x7fffaf75aff3  libdscsym.dylib (148.3) <EF99B991-18D9-3A73-BE88-4DC55F036DEF> /usr/lib/libdscsym.dylib\r\n    0x7fffaf77c000 -     0x7fffaf77cfff  libenergytrace.dylib (15) <A1B040A2-7977-3097-9ADF-34FF181EB970> /usr/lib/libenergytrace.dylib\r\n    0x7fffaf78c000 -     0x7fffaf791ff7  libheimdal-asn1.dylib (498.50.8) <A40E3196-235E-34CE-AD9A-8D1AFC5DE004> /usr/lib/libheimdal-asn1.dylib\r\n    0x7fffaf792000 -     0x7fffaf884ff7  libiconv.2.dylib (50) <42125B35-81D7-3FC4-9475-A26DBE10884D> /usr/lib/libiconv.2.dylib\r\n    0x7fffaf885000 -     0x7fffafaaaffb  libicucore.A.dylib (57166.0.1) <CCD2ED24-3071-383B-925D-8D763BB12A6F> /usr/lib/libicucore.A.dylib\r\n    0x7fffafab0000 -     0x7fffafab1fff  liblangid.dylib (126) <2085E7A7-9A34-3735-87F4-F174EF8EABF0> /usr/lib/liblangid.dylib\r\n    0x7fffafab2000 -     0x7fffafacbffb  liblzma.5.dylib (10) <44BD0279-99DD-36B5-8A6E-C11432E2098D> /usr/lib/liblzma.5.dylib\r\n    0x7fffafacc000 -     0x7fffafae2ff7  libmarisa.dylib (5) <9030D214-5D0F-30CB-AC03-902C63909362> /usr/lib/libmarisa.dylib\r\n    0x7fffafae3000 -     0x7fffafd8bff7  libmecabra.dylib (744.8) <D429FCC9-42A4-38B3-8784-44024BC859EF> /usr/lib/libmecabra.dylib\r\n    0x7fffafdbe000 -     0x7fffafe38ff3  libnetwork.dylib (856.60.1) <191E99F5-4723-3180-8013-02AF2F9AE4B8> /usr/lib/libnetwork.dylib\r\n    0x7fffafe39000 -     0x7fffb020b047  libobjc.A.dylib (709.1) <70614861-0340-32E2-85ED-FE65759CDFFA> /usr/lib/libobjc.A.dylib\r\n    0x7fffb020e000 -     0x7fffb0212fff  libpam.2.dylib (21.30.1) <71EB0D88-DE84-3C8D-A2C5-58AA282BC5BC> /usr/lib/libpam.2.dylib\r\n    0x7fffb0213000 -     0x7fffb0244fff  libpcap.A.dylib (67.60.1) <F6BC6ED6-AEE4-3520-B248-0C342636E2B0> /usr/lib/libpcap.A.dylib\r\n    0x7fffb0261000 -     0x7fffb027dffb  libresolv.9.dylib (64) <A244AE4C-00B0-396C-98FF-97FE4DB3DA30> /usr/lib/libresolv.9.dylib\r\n    0x7fffb027e000 -     0x7fffb02b7fff  libsandbox.1.dylib (592.70.1) <9C428C49-9D9A-3F9F-9573-BF5202D1F933> /usr/lib/libsandbox.1.dylib\r\n    0x7fffb02cb000 -     0x7fffb02ccff3  libspindump.dylib (231.3) <C7CEEB64-06F4-3ACA-AAC1-30ECA909501A> /usr/lib/libspindump.dylib\r\n    0x7fffb02cd000 -     0x7fffb041bff7  libsqlite3.dylib (254.8) <1ECF7DF7-7A07-3B4B-A63B-F4EFF6BC7ACF> /usr/lib/libsqlite3.dylib\r\n    0x7fffb0477000 -     0x7fffb04c7fff  libstdc++.6.dylib (104.1) <A980E08C-A511-3D19-9881-1D79B7CFF2BA> /usr/lib/libstdc++.6.dylib\r\n    0x7fffb0510000 -     0x7fffb051dfff  libxar.1.dylib (357) <69547C64-E811-326F-BBED-490C6361BDCB> /usr/lib/libxar.1.dylib\r\n    0x7fffb051e000 -     0x7fffb060dfff  libxml2.2.dylib (30.17) <D4C3E467-37C6-34DE-8A1B-E7BFC29985E9> /usr/lib/libxml2.2.dylib\r\n    0x7fffb060e000 -     0x7fffb0637fff  libxslt.1.dylib (15.9) <00735AD5-B62D-3E83-86AC-5533E4E2B102> /usr/lib/libxslt.1.dylib\r\n    0x7fffb0638000 -     0x7fffb0649ff3  libz.1.dylib (67) <46E3FFA2-4328-327A-8D34-A03E20BFFB8E> /usr/lib/libz.1.dylib\r\n    0x7fffb0658000 -     0x7fffb065cff7  libcache.dylib (79) <093A4DAB-8385-3D47-A350-E20CB7CCF7BF> /usr/lib/system/libcache.dylib\r\n    0x7fffb065d000 -     0x7fffb0667fff  libcommonCrypto.dylib (60092.50.5) <8A64D1B0-C70E-385C-92F0-E669079FDA90> /usr/lib/system/libcommonCrypto.dylib\r\n    0x7fffb0668000 -     0x7fffb066ffff  libcompiler_rt.dylib (62) <55D47421-772A-32AB-B529-1A46C2F43B4D> /usr/lib/system/libcompiler_rt.dylib\r\n    0x7fffb0670000 -     0x7fffb0678fff  libcopyfile.dylib (138) <819BEA3C-DF11-3E3D-A1A1-5A51C5BF1961> /usr/lib/system/libcopyfile.dylib\r\n    0x7fffb0679000 -     0x7fffb06fcfdf  libcorecrypto.dylib (442.50.19) <65D7165E-2E71-335D-A2D6-33F78E2DF0C1> /usr/lib/system/libcorecrypto.dylib\r\n    0x7fffb06fd000 -     0x7fffb072efff  libdispatch.dylib (703.50.37) <6582BAD6-ED27-3B30-B620-90B1C5A4AE3C> /usr/lib/system/libdispatch.dylib\r\n    0x7fffb072f000 -     0x7fffb0734ffb  libdyld.dylib (433.5) <9B2AC56D-107C-3541-A127-9094A751F2C9> /usr/lib/system/libdyld.dylib\r\n    0x7fffb0735000 -     0x7fffb0735ffb  libkeymgr.dylib (28) <7AA011A9-DC21-3488-BF73-3B5B14D1FDD6> /usr/lib/system/libkeymgr.dylib\r\n    0x7fffb0736000 -     0x7fffb0742fff  libkxld.dylib (3789.70.16) <BD02EF3F-6E09-3A64-AB21-553D5B1D2859> /usr/lib/system/libkxld.dylib\r\n    0x7fffb0743000 -     0x7fffb0743fff  liblaunch.dylib (972.70.1) <B856ABD2-896E-3DE0-B2C8-146A6AF8E2A7> /usr/lib/system/liblaunch.dylib\r\n    0x7fffb0744000 -     0x7fffb0749ff3  libmacho.dylib (898) <17D5D855-F6C3-3B04-B680-E9BF02EF8AED> /usr/lib/system/libmacho.dylib\r\n    0x7fffb074a000 -     0x7fffb074cff3  libquarantine.dylib (85.50.1) <12448CC2-378E-35F3-BE33-9DC395A5B970> /usr/lib/system/libquarantine.dylib\r\n    0x7fffb074d000 -     0x7fffb074effb  libremovefile.dylib (45) <38D4CB9C-10CD-30D3-8B7B-A515EC75FE85> /usr/lib/system/libremovefile.dylib\r\n    0x7fffb074f000 -     0x7fffb0767ff7  libsystem_asl.dylib (349.50.5) <096E4228-3B7C-30A6-8B13-EC909A64499A> /usr/lib/system/libsystem_asl.dylib\r\n    0x7fffb0768000 -     0x7fffb0768ff7  libsystem_blocks.dylib (67) <10DC5404-73AB-35B3-A277-A8AFECB476EB> /usr/lib/system/libsystem_blocks.dylib\r\n    0x7fffb0769000 -     0x7fffb07f6fef  libsystem_c.dylib (1158.50.2) <E5AE5244-7D0C-36AC-8BB6-C7AE7EA52A4B> /usr/lib/system/libsystem_c.dylib\r\n    0x7fffb07f7000 -     0x7fffb07faffb  libsystem_configuration.dylib (888.60.2) <BECC01A2-CA8D-31E6-BCDF-D452965FA976> /usr/lib/system/libsystem_configuration.dylib\r\n    0x7fffb07fb000 -     0x7fffb07fefff  libsystem_coreservices.dylib (41.4) <7D26DE79-B424-3450-85E1-F7FAB32714AB> /usr/lib/system/libsystem_coreservices.dylib\r\n    0x7fffb07ff000 -     0x7fffb0817fff  libsystem_coretls.dylib (121.50.4) <EC6FCF07-DCFB-3A03-9CC9-6DD3709974C6> /usr/lib/system/libsystem_coretls.dylib\r\n    0x7fffb0818000 -     0x7fffb081efff  libsystem_dnssd.dylib (765.50.9) <CC960215-0B1B-3822-A13A-3DDE96FA796F> /usr/lib/system/libsystem_dnssd.dylib\r\n    0x7fffb081f000 -     0x7fffb0848ff7  libsystem_info.dylib (503.50.4) <611DB84C-BF70-3F92-8702-B9F28A900920> /usr/lib/system/libsystem_info.dylib\r\n    0x7fffb0849000 -     0x7fffb086bff7  libsystem_kernel.dylib (3789.70.16) <34B1F16C-BC9C-3C5F-9045-0CAE91CB5914> /usr/lib/system/libsystem_kernel.dylib\r\n    0x7fffb086c000 -     0x7fffb08b3fe7  libsystem_m.dylib (3121.6) <86D499B5-BBDC-3D3B-8A4E-97AE8E6672A4> /usr/lib/system/libsystem_m.dylib\r\n    0x7fffb08b4000 -     0x7fffb08d2ff7  libsystem_malloc.dylib (116.50.8) <A3D15F17-99A6-3367-8C7E-4280E8619C95> /usr/lib/system/libsystem_malloc.dylib\r\n    0x7fffb08d3000 -     0x7fffb092cffb  libsystem_network.dylib (856.60.1) <369D0221-56CA-3C3E-9EDE-94B41CAE77B7> /usr/lib/system/libsystem_network.dylib\r\n    0x7fffb092d000 -     0x7fffb0936ff3  libsystem_networkextension.dylib (563.60.2) <B021F2B3-8A75-3633-ABB0-FC012B8E9B0C> /usr/lib/system/libsystem_networkextension.dylib\r\n    0x7fffb0937000 -     0x7fffb0940ff3  libsystem_notify.dylib (165.20.1) <B8160190-A069-3B3A-BDF6-2AA408221FAE> /usr/lib/system/libsystem_notify.dylib\r\n    0x7fffb0941000 -     0x7fffb0949fe7  libsystem_platform.dylib (126.50.8) <897462FD-B318-321B-A554-E61982630F7E> /usr/lib/system/libsystem_platform.dylib\r\n    0x7fffb094a000 -     0x7fffb0954ff7  libsystem_pthread.dylib (218.60.3) <B8FB5E20-3295-39E2-B5EB-B464D1D4B104> /usr/lib/system/libsystem_pthread.dylib\r\n    0x7fffb0955000 -     0x7fffb0958ff7  libsystem_sandbox.dylib (592.70.1) <4B92EC49-ACD0-36AE-B07A-A2B8152EAF9D> /usr/lib/system/libsystem_sandbox.dylib\r\n    0x7fffb0959000 -     0x7fffb095aff3  libsystem_secinit.dylib (24.50.4) <F78B847B-3565-3E4B-98A6-F7AD40392E2D> /usr/lib/system/libsystem_secinit.dylib\r\n    0x7fffb095b000 -     0x7fffb0962ffb  libsystem_symptoms.dylib (532.50.47) <3390E07C-C1CE-348F-ADBD-2C5440B45EAA> /usr/lib/system/libsystem_symptoms.dylib\r\n    0x7fffb0963000 -     0x7fffb0976ff7  libsystem_trace.dylib (518.70.1) <AC63A7FE-50D9-3A30-96E6-F6B7FF16E465> /usr/lib/system/libsystem_trace.dylib\r\n    0x7fffb0977000 -     0x7fffb097cffb  libunwind.dylib (35.3) <3D50D8A8-C460-334D-A519-2DA841102C6B> /usr/lib/system/libunwind.dylib\r\n    0x7fffb097d000 -     0x7fffb09a6ff7  libxpc.dylib (972.70.1) <BF896DF0-D8E9-31A8-A4B3-01120BFEEE52> /usr/lib/system/libxpc.dylib\r\n\r\nExternal Modification Summary:\r\n  Calls made by other processes targeting this process:\r\n    task_for_pid: 0\r\n    thread_create: 0\r\n    thread_set_state: 0\r\n  Calls made by this process:\r\n    task_for_pid: 0\r\n    thread_create: 0\r\n    thread_set_state: 0\r\n  Calls made by all processes on this machine:\r\n    task_for_pid: 53754\r\n    thread_create: 0\r\n    thread_set_state: 0\r\n\r\nVM Region Summary:\r\nReadOnly portion of Libraries: Total=507.6M resident=0K(0%) swapped_out_or_unallocated=507.6M(100%)\r\nWritable regions: Total=630.4M written=0K(0%) resident=0K(0%) swapped_out=0K(0%) unallocated=630.4M(100%)\r\n```"}}
{"actor":{"display_login":"andyu78945"}}