Open andyu78945 opened 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 Binary Images: 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 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 0x10d45d000 - 0x10d46fff3 libTraditionalChineseConverter.dylib (69) <62B80E34-CB09-360D-9CD0-959C59756869> /System/Library/CoreServices/Encodings/libTraditionalChineseConverter.dylib 0x10d5e5000 - 0x10d5e7fff +_locale.so (0) <11DBC6F3-6AD2-309D-B664-EF36341434DB> 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I am having exactly the same issue - did you manage to solve it?
@andyu78945 Hi. Did you solved it. Now I have the same problem.
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.
python error