AttributeError: 'module' object has no attribute 'text_format'

請注意，本類僅僅是記錄開發過程中遇到對問題，可能會亂貼代碼，亂貼圖，亂貼報錯信息，不保證能解決問題，以及有優美的排版，後面有時間我會重新整理的。
解決方法

sudo pip install protobuf==2.6.0

注意：參考的文章請點這裏

簡略報錯信息

./experiments/scripts/faster_rcnn_alt_opt.sh 0 ZF pascal_vocProcess Process-1:Traceback (most recent call last):  File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap    self.run()  File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run    self._target(*self._args, **self._kwargs)  File "./tools/train_faster_rcnn_alt_opt.py", line 129, in train_rpn    max_iters=max_iters)  File "/home/pikachu/dev/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 157, in train_net    pretrained_model=pretrained_model)  File "/home/pikachu/dev/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 51, in __init__    pb2.text_format.Merge(f.read(), self.solver_param)AttributeError: 'module' object has no attribute 'text_format'

詳細的報錯信息

pikachu@pikachu-Aspire-VN7-591G:~/dev/py-faster-rcnn$ ./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=40000++ date +%Y-%m-%d_%H-%M-%S+ LOG=experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-07-07_10-46-59+ exec++ tee -a experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-07-07_10-46-59+ echo Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-07-07_10-46-59Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-07-07_10-46-59+ ./tools/train_faster_rcnn_alt_opt.py --gpu 0 --net_name ZF --weights data/imagenet_models/ZF.v2.caffemodel --imdb voc_2007_trainval --cfg experiments/cfgs/faster_rcnn_alt_opt.ymlCalled with args:Namespace(cfg_file='experiments/cfgs/faster_rcnn_alt_opt.yml', gpu_id=0, imdb_name='voc_2007_trainval', net_name='ZF', pretrained_model='data/imagenet_models/ZF.v2.caffemodel', set_cfgs=None)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Stage 1 RPN, init from ImageNet model~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Init model: data/imagenet_models/ZF.v2.caffemodelUsing config:{'DATA_DIR': '/home/pikachu/dev/py-faster-rcnn/data', 'DEDUP_BOXES': 0.0625, 'EPS': 1e-14, 'EXP_DIR': 'faster_rcnn_alt_opt', 'GPU_ID': 0, 'MATLAB': 'matlab', 'MODELS_DIR': '/home/pikachu/dev/py-faster-rcnn/models/pascal_voc', 'PIXEL_MEANS': array([[[ 102.9801,  115.9465,  122.7717]]]), 'RNG_SEED': 3, 'ROOT_DIR': '/home/pikachu/dev/py-faster-rcnn', 'TEST': {'BBOX_REG': True,          'HAS_RPN': True,          'MAX_SIZE': 1000,          'NMS': 0.3,          'PROPOSAL_METHOD': 'selective_search',          'RPN_MIN_SIZE': 16,          'RPN_NMS_THRESH': 0.7,          'RPN_POST_NMS_TOP_N': 300,          'RPN_PRE_NMS_TOP_N': 6000,          'SCALES': [600],          'SVM': False}, 'TRAIN': {'ASPECT_GROUPING': True,           'BATCH_SIZE': 128,           'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],           'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0],           'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2],           'BBOX_NORMALIZE_TARGETS': True,           'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': False,           'BBOX_REG': False,           'BBOX_THRESH': 0.5,           'BG_THRESH_HI': 0.5,           'BG_THRESH_LO': 0.0,           'FG_FRACTION': 0.25,           'FG_THRESH': 0.5,           'HAS_RPN': True,           'IMS_PER_BATCH': 1,           'MAX_SIZE': 1000,           'PROPOSAL_METHOD': 'gt',           'RPN_BATCHSIZE': 256,           'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0],           'RPN_CLOBBER_POSITIVES': False,           'RPN_FG_FRACTION': 0.5,           'RPN_MIN_SIZE': 16,           'RPN_NEGATIVE_OVERLAP': 0.3,           'RPN_NMS_THRESH': 0.7,           'RPN_POSITIVE_OVERLAP': 0.7,           'RPN_POSITIVE_WEIGHT': -1.0,           'RPN_POST_NMS_TOP_N': 2000,           'RPN_PRE_NMS_TOP_N': 12000,           'SCALES': [600],           'SNAPSHOT_INFIX': 'stage1',           'SNAPSHOT_ITERS': 10000,           'USE_FLIPPED': True,           'USE_PREFETCH': False}, 'USE_GPU_NMS': True}Loaded dataset `voc_2007_trainval` for trainingSet proposal method: gtAppending horizontally-flipped training examples...wrote gt roidb to /home/pikachu/dev/py-faster-rcnn/data/cache/voc_2007_trainval_gt_roidb.pkldonePreparing training data...doneroidb len: 10022Output will be saved to `/home/pikachu/dev/py-faster-rcnn/output/faster_rcnn_alt_opt/voc_2007_trainval`Filtered 0 roidb entries: 10022 -> 10022WARNING: Logging before InitGoogleLogging() is written to STDERRI0707 10:47:17.331874  3142 solver.cpp:44] Initializing solver from parameters: train_net: "models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt"base_lr: 0.001display: 20lr_policy: "step"gamma: 0.1momentum: 0.9weight_decay: 0.0005stepsize: 60000snapshot: 0snapshot_prefix: "zf_rpn"average_loss: 100I0707 10:47:17.331914  3142 solver.cpp:77] Creating training net from train_net file: models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.ptI0707 10:47:17.332700  3142 net.cpp:51] Initializing net from parameters: name: "ZF"state {  phase: TRAIN}layer {  name: "input-data"  type: "Python"  top: "data"  top: "im_info"  top: "gt_boxes"  python_param {    module: "roi_data_layer.layer"    layer: "RoIDataLayer"    param_str: "\'num_classes\': 21"  }}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"}I0707 10:47:17.332875  3142 layer_factory.hpp:77] Creating layer input-dataI0707 10:47:17.363018  3142 net.cpp:84] Creating Layer input-dataI0707 10:47:17.363045  3142 net.cpp:380] input-data -> dataI0707 10:47:17.374701  3142 net.cpp:380] input-data -> im_infoI0707 10:47:17.374732  3142 net.cpp:380] input-data -> gt_boxesRoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1}I0707 10:47:17.401430  3142 net.cpp:122] Setting up input-dataI0707 10:47:17.401459  3142 net.cpp:129] Top shape: 1 3 600 1000 (1800000)I0707 10:47:17.401475  3142 net.cpp:129] Top shape: 1 3 (3)I0707 10:47:17.401480  3142 net.cpp:129] Top shape: 1 4 (4)I0707 10:47:17.401484  3142 net.cpp:137] Memory required for data: 7200028I0707 10:47:17.401494  3142 layer_factory.hpp:77] Creating layer data_input-data_0_splitI0707 10:47:17.401506  3142 net.cpp:84] Creating Layer data_input-data_0_splitI0707 10:47:17.401511  3142 net.cpp:406] data_input-data_0_split <- dataI0707 10:47:17.401522  3142 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_0I0707 10:47:17.401533  3142 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_1I0707 10:47:17.401567  3142 net.cpp:122] Setting up data_input-data_0_splitI0707 10:47:17.401576  3142 net.cpp:129] Top shape: 1 3 600 1000 (1800000)I0707 10:47:17.401584  3142 net.cpp:129] Top shape: 1 3 600 1000 (1800000)I0707 10:47:17.401587  3142 net.cpp:137] Memory required for data: 21600028I0707 10:47:17.401590  3142 layer_factory.hpp:77] Creating layer conv1I0707 10:47:17.401612  3142 net.cpp:84] Creating Layer conv1I0707 10:47:17.401617  3142 net.cpp:406] conv1 <- data_input-data_0_split_0I0707 10:47:17.401621  3142 net.cpp:380] conv1 -> conv1I0707 10:47:21.584990  3142 net.cpp:122] Setting up conv1I0707 10:47:21.585019  3142 net.cpp:129] Top shape: 1 96 300 500 (14400000)I0707 10:47:21.585022  3142 net.cpp:137] Memory required for data: 79200028I0707 10:47:21.585036  3142 layer_factory.hpp:77] Creating layer relu1I0707 10:47:21.585047  3142 net.cpp:84] Creating Layer relu1I0707 10:47:21.585049  3142 net.cpp:406] relu1 <- conv1I0707 10:47:21.585054  3142 net.cpp:367] relu1 -> conv1 (in-place)I0707 10:47:21.585517  3142 net.cpp:122] Setting up relu1I0707 10:47:21.585530  3142 net.cpp:129] Top shape: 1 96 300 500 (14400000)I0707 10:47:21.585531  3142 net.cpp:137] Memory required for data: 136800028I0707 10:47:21.585535  3142 layer_factory.hpp:77] Creating layer norm1I0707 10:47:21.585546  3142 net.cpp:84] Creating Layer norm1I0707 10:47:21.585549  3142 net.cpp:406] norm1 <- conv1I0707 10:47:21.585553  3142 net.cpp:380] norm1 -> norm1I0707 10:47:21.585670  3142 net.cpp:122] Setting up norm1I0707 10:47:21.585677  3142 net.cpp:129] Top shape: 1 96 300 500 (14400000)I0707 10:47:21.585680  3142 net.cpp:137] Memory required for data: 194400028I0707 10:47:21.585681  3142 layer_factory.hpp:77] Creating layer pool1I0707 10:47:21.585686  3142 net.cpp:84] Creating Layer pool1I0707 10:47:21.585688  3142 net.cpp:406] pool1 <- norm1I0707 10:47:21.585692  3142 net.cpp:380] pool1 -> pool1I0707 10:47:21.585722  3142 net.cpp:122] Setting up pool1I0707 10:47:21.585727  3142 net.cpp:129] Top shape: 1 96 151 251 (3638496)I0707 10:47:21.585729  3142 net.cpp:137] Memory required for data: 208954012I0707 10:47:21.585731  3142 layer_factory.hpp:77] Creating layer conv2I0707 10:47:21.585739  3142 net.cpp:84] Creating Layer conv2I0707 10:47:21.585746  3142 net.cpp:406] conv2 <- pool1I0707 10:47:21.585749  3142 net.cpp:380] conv2 -> conv2I0707 10:47:21.600478  3142 net.cpp:122] Setting up conv2I0707 10:47:21.600505  3142 net.cpp:129] Top shape: 1 256 76 126 (2451456)I0707 10:47:21.600509  3142 net.cpp:137] Memory required for data: 218759836I0707 10:47:21.600523  3142 layer_factory.hpp:77] Creating layer relu2I0707 10:47:21.600533  3142 net.cpp:84] Creating Layer relu2I0707 10:47:21.600543  3142 net.cpp:406] relu2 <- conv2I0707 10:47:21.600548  3142 net.cpp:367] relu2 -> conv2 (in-place)I0707 10:47:21.600803  3142 net.cpp:122] Setting up relu2I0707 10:47:21.600814  3142 net.cpp:129] Top shape: 1 256 76 126 (2451456)I0707 10:47:21.600817  3142 net.cpp:137] Memory required for data: 228565660I0707 10:47:21.600819  3142 layer_factory.hpp:77] Creating layer norm2I0707 10:47:21.600828  3142 net.cpp:84] Creating Layer norm2I0707 10:47:21.600831  3142 net.cpp:406] norm2 <- conv2I0707 10:47:21.600836  3142 net.cpp:380] norm2 -> norm2I0707 10:47:21.600947  3142 net.cpp:122] Setting up norm2I0707 10:47:21.600955  3142 net.cpp:129] Top shape: 1 256 76 126 (2451456)I0707 10:47:21.600957  3142 net.cpp:137] Memory required for data: 238371484I0707 10:47:21.600960  3142 layer_factory.hpp:77] Creating layer pool2I0707 10:47:21.600965  3142 net.cpp:84] Creating Layer pool2I0707 10:47:21.600966  3142 net.cpp:406] pool2 <- norm2I0707 10:47:21.600970  3142 net.cpp:380] pool2 -> pool2I0707 10:47:21.601002  3142 net.cpp:122] Setting up pool2I0707 10:47:21.601007  3142 net.cpp:129] Top shape: 1 256 39 64 (638976)I0707 10:47:21.601011  3142 net.cpp:137] Memory required for data: 240927388I0707 10:47:21.601012  3142 layer_factory.hpp:77] Creating layer conv3I0707 10:47:21.601019  3142 net.cpp:84] Creating Layer conv3I0707 10:47:21.601023  3142 net.cpp:406] conv3 <- pool2I0707 10:47:21.601027  3142 net.cpp:380] conv3 -> conv3I0707 10:47:21.615322  3142 net.cpp:122] Setting up conv3I0707 10:47:21.615350  3142 net.cpp:129] Top shape: 1 384 39 64 (958464)I0707 10:47:21.615352  3142 net.cpp:137] Memory required for data: 244761244I0707 10:47:21.615366  3142 layer_factory.hpp:77] Creating layer relu3I0707 10:47:21.615375  3142 net.cpp:84] Creating Layer relu3I0707 10:47:21.615378  3142 net.cpp:406] relu3 <- conv3I0707 10:47:21.615386  3142 net.cpp:367] relu3 -> conv3 (in-place)I0707 10:47:21.615893  3142 net.cpp:122] Setting up relu3I0707 10:47:21.615907  3142 net.cpp:129] Top shape: 1 384 39 64 (958464)I0707 10:47:21.615911  3142 net.cpp:137] Memory required for data: 248595100I0707 10:47:21.615913  3142 layer_factory.hpp:77] Creating layer conv4I0707 10:47:21.615923  3142 net.cpp:84] Creating Layer conv4I0707 10:47:21.615926  3142 net.cpp:406] conv4 <- conv3I0707 10:47:21.615932  3142 net.cpp:380] conv4 -> conv4I0707 10:47:21.620095  3142 net.cpp:122] Setting up conv4I0707 10:47:21.620124  3142 net.cpp:129] Top shape: 1 384 39 64 (958464)I0707 10:47:21.620127  3142 net.cpp:137] Memory required for data: 252428956I0707 10:47:21.620136  3142 layer_factory.hpp:77] Creating layer relu4I0707 10:47:21.620146  3142 net.cpp:84] Creating Layer relu4I0707 10:47:21.620149  3142 net.cpp:406] relu4 <- conv4I0707 10:47:21.620154  3142 net.cpp:367] relu4 -> conv4 (in-place)I0707 10:47:21.620321  3142 net.cpp:122] Setting up relu4I0707 10:47:21.620329  3142 net.cpp:129] Top shape: 1 384 39 64 (958464)I0707 10:47:21.620332  3142 net.cpp:137] Memory required for data: 256262812I0707 10:47:21.620333  3142 layer_factory.hpp:77] Creating layer conv5I0707 10:47:21.620340  3142 net.cpp:84] Creating Layer conv5I0707 10:47:21.620343  3142 net.cpp:406] conv5 <- conv4I0707 10:47:21.620348  3142 net.cpp:380] conv5 -> conv5I0707 10:47:21.623618  3142 net.cpp:122] Setting up conv5I0707 10:47:21.623647  3142 net.cpp:129] Top shape: 1 256 39 64 (638976)I0707 10:47:21.623651  3142 net.cpp:137] Memory required for data: 258818716I0707 10:47:21.623662  3142 layer_factory.hpp:77] Creating layer relu5I0707 10:47:21.623670  3142 net.cpp:84] Creating Layer relu5I0707 10:47:21.623673  3142 net.cpp:406] relu5 <- conv5I0707 10:47:21.623690  3142 net.cpp:367] relu5 -> conv5 (in-place)I0707 10:47:21.624076  3142 net.cpp:122] Setting up relu5I0707 10:47:21.624089  3142 net.cpp:129] Top shape: 1 256 39 64 (638976)I0707 10:47:21.624092  3142 net.cpp:137] Memory required for data: 261374620I0707 10:47:21.624095  3142 layer_factory.hpp:77] Creating layer rpn_conv1I0707 10:47:21.624114  3142 net.cpp:84] Creating Layer rpn_conv1I0707 10:47:21.624120  3142 net.cpp:406] rpn_conv1 <- conv5I0707 10:47:21.624130  3142 net.cpp:380] rpn_conv1 -> rpn_conv1I0707 10:47:21.631552  3142 net.cpp:122] Setting up rpn_conv1I0707 10:47:21.631582  3142 net.cpp:129] Top shape: 1 256 39 64 (638976)I0707 10:47:21.631585  3142 net.cpp:137] Memory required for data: 263930524I0707 10:47:21.631597  3142 layer_factory.hpp:77] Creating layer rpn_relu1I0707 10:47:21.631608  3142 net.cpp:84] Creating Layer rpn_relu1I0707 10:47:21.631614  3142 net.cpp:406] rpn_relu1 <- rpn_conv1I0707 10:47:21.631623  3142 net.cpp:367] rpn_relu1 -> rpn_conv1 (in-place)I0707 10:47:21.632009  3142 net.cpp:122] Setting up rpn_relu1I0707 10:47:21.632019  3142 net.cpp:129] Top shape: 1 256 39 64 (638976)I0707 10:47:21.632024  3142 net.cpp:137] Memory required for data: 266486428I0707 10:47:21.632027  3142 layer_factory.hpp:77] Creating layer rpn_conv1_rpn_relu1_0_splitI0707 10:47:21.632035  3142 net.cpp:84] Creating Layer rpn_conv1_rpn_relu1_0_splitI0707 10:47:21.632040  3142 net.cpp:406] rpn_conv1_rpn_relu1_0_split <- rpn_conv1I0707 10:47:21.632048  3142 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_0I0707 10:47:21.632058  3142 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_1I0707 10:47:21.632105  3142 net.cpp:122] Setting up rpn_conv1_rpn_relu1_0_splitI0707 10:47:21.632112  3142 net.cpp:129] Top shape: 1 256 39 64 (638976)I0707 10:47:21.632118  3142 net.cpp:129] Top shape: 1 256 39 64 (638976)I0707 10:47:21.632122  3142 net.cpp:137] Memory required for data: 271598236I0707 10:47:21.632127  3142 layer_factory.hpp:77] Creating layer rpn_cls_scoreI0707 10:47:21.632139  3142 net.cpp:84] Creating Layer rpn_cls_scoreI0707 10:47:21.632143  3142 net.cpp:406] rpn_cls_score <- rpn_conv1_rpn_relu1_0_split_0I0707 10:47:21.632151  3142 net.cpp:380] rpn_cls_score -> rpn_cls_scoreI0707 10:47:21.633322  3142 net.cpp:122] Setting up rpn_cls_scoreI0707 10:47:21.633334  3142 net.cpp:129] Top shape: 1 18 39 64 (44928)I0707 10:47:21.633338  3142 net.cpp:137] Memory required for data: 271777948I0707 10:47:21.633345  3142 layer_factory.hpp:77] Creating layer rpn_cls_score_rpn_cls_score_0_splitI0707 10:47:21.633355  3142 net.cpp:84] Creating Layer rpn_cls_score_rpn_cls_score_0_splitI0707 10:47:21.633360  3142 net.cpp:406] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_scoreI0707 10:47:21.633368  3142 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0I0707 10:47:21.633378  3142 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1I0707 10:47:21.633417  3142 net.cpp:122] Setting up rpn_cls_score_rpn_cls_score_0_splitI0707 10:47:21.633424  3142 net.cpp:129] Top shape: 1 18 39 64 (44928)I0707 10:47:21.633426  3142 net.cpp:129] Top shape: 1 18 39 64 (44928)I0707 10:47:21.633429  3142 net.cpp:137] Memory required for data: 272137372I0707 10:47:21.633430  3142 layer_factory.hpp:77] Creating layer rpn_bbox_predI0707 10:47:21.633437  3142 net.cpp:84] Creating Layer rpn_bbox_predI0707 10:47:21.633441  3142 net.cpp:406] rpn_bbox_pred <- rpn_conv1_rpn_relu1_0_split_1I0707 10:47:21.633445  3142 net.cpp:380] rpn_bbox_pred -> rpn_bbox_predI0707 10:47:21.634851  3142 net.cpp:122] Setting up rpn_bbox_predI0707 10:47:21.634863  3142 net.cpp:129] Top shape: 1 36 39 64 (89856)I0707 10:47:21.634866  3142 net.cpp:137] Memory required for data: 272496796I0707 10:47:21.634891  3142 layer_factory.hpp:77] Creating layer rpn_cls_score_reshapeI0707 10:47:21.634905  3142 net.cpp:84] Creating Layer rpn_cls_score_reshapeI0707 10:47:21.634912  3142 net.cpp:406] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0I0707 10:47:21.634922  3142 net.cpp:380] rpn_cls_score_reshape -> rpn_cls_score_reshapeI0707 10:47:21.634954  3142 net.cpp:122] Setting up rpn_cls_score_reshapeI0707 10:47:21.634959  3142 net.cpp:129] Top shape: 1 2 351 64 (44928)I0707 10:47:21.634964  3142 net.cpp:137] Memory required for data: 272676508I0707 10:47:21.634968  3142 layer_factory.hpp:77] Creating layer rpn-dataI0707 10:47:21.660670  3142 net.cpp:84] Creating Layer rpn-dataI0707 10:47:21.660693  3142 net.cpp:406] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1I0707 10:47:21.660701  3142 net.cpp:406] rpn-data <- gt_boxesI0707 10:47:21.660704  3142 net.cpp:406] rpn-data <- im_infoI0707 10:47:21.660707  3142 net.cpp:406] rpn-data <- data_input-data_0_split_1I0707 10:47:21.660712  3142 net.cpp:380] rpn-data -> rpn_labelsI0707 10:47:21.660722  3142 net.cpp:380] rpn-data -> rpn_bbox_targetsI0707 10:47:21.660732  3142 net.cpp:380] rpn-data -> rpn_bbox_inside_weightsI0707 10:47:21.660744  3142 net.cpp:380] rpn-data -> rpn_bbox_outside_weightsI0707 10:47:21.661968  3142 net.cpp:122] Setting up rpn-dataI0707 10:47:21.661985  3142 net.cpp:129] Top shape: 1 1 351 64 (22464)I0707 10:47:21.661988  3142 net.cpp:129] Top shape: 1 36 39 64 (89856)I0707 10:47:21.661991  3142 net.cpp:129] Top shape: 1 36 39 64 (89856)I0707 10:47:21.661993  3142 net.cpp:129] Top shape: 1 36 39 64 (89856)I0707 10:47:21.661995  3142 net.cpp:137] Memory required for data: 273844636I0707 10:47:21.661999  3142 layer_factory.hpp:77] Creating layer rpn_loss_clsI0707 10:47:21.662017  3142 net.cpp:84] Creating Layer rpn_loss_clsI0707 10:47:21.662022  3142 net.cpp:406] rpn_loss_cls <- rpn_cls_score_reshapeI0707 10:47:21.662026  3142 net.cpp:406] rpn_loss_cls <- rpn_labelsI0707 10:47:21.662032  3142 net.cpp:380] rpn_loss_cls -> rpn_cls_lossI0707 10:47:21.662044  3142 layer_factory.hpp:77] Creating layer rpn_loss_clsI0707 10:47:21.662364  3142 net.cpp:122] Setting up rpn_loss_clsI0707 10:47:21.662371  3142 net.cpp:129] Top shape: (1)I0707 10:47:21.662374  3142 net.cpp:132]     with loss weight 1I0707 10:47:21.662381  3142 net.cpp:137] Memory required for data: 273844640I0707 10:47:21.662384  3142 layer_factory.hpp:77] Creating layer rpn_loss_bboxI0707 10:47:21.662395  3142 net.cpp:84] Creating Layer rpn_loss_bboxI0707 10:47:21.662401  3142 net.cpp:406] rpn_loss_bbox <- rpn_bbox_predI0707 10:47:21.662405  3142 net.cpp:406] rpn_loss_bbox <- rpn_bbox_targetsI0707 10:47:21.662410  3142 net.cpp:406] rpn_loss_bbox <- rpn_bbox_inside_weightsI0707 10:47:21.662415  3142 net.cpp:406] rpn_loss_bbox <- rpn_bbox_outside_weightsI0707 10:47:21.662421  3142 net.cpp:380] rpn_loss_bbox -> rpn_loss_bboxI0707 10:47:21.663342  3142 net.cpp:122] Setting up rpn_loss_bboxI0707 10:47:21.663352  3142 net.cpp:129] Top shape: (1)I0707 10:47:21.663353  3142 net.cpp:132]     with loss weight 1I0707 10:47:21.663359  3142 net.cpp:137] Memory required for data: 273844644I0707 10:47:21.663363  3142 layer_factory.hpp:77] Creating layer dummy_roi_pool_conv5I0707 10:47:21.663373  3142 net.cpp:84] Creating Layer dummy_roi_pool_conv5I0707 10:47:21.663380  3142 net.cpp:380] dummy_roi_pool_conv5 -> dummy_roi_pool_conv5I0707 10:47:21.663417  3142 net.cpp:122] Setting up dummy_roi_pool_conv5I0707 10:47:21.663424  3142 net.cpp:129] Top shape: 1 9216 (9216)I0707 10:47:21.663426  3142 net.cpp:137] Memory required for data: 273881508I0707 10:47:21.663429  3142 layer_factory.hpp:77] Creating layer fc6I0707 10:47:21.663437  3142 net.cpp:84] Creating Layer fc6I0707 10:47:21.663439  3142 net.cpp:406] fc6 <- dummy_roi_pool_conv5I0707 10:47:21.663444  3142 net.cpp:380] fc6 -> fc6I0707 10:47:21.735376  3142 net.cpp:122] Setting up fc6I0707 10:47:21.735415  3142 net.cpp:129] Top shape: 1 4096 (4096)I0707 10:47:21.735419  3142 net.cpp:137] Memory required for data: 273897892I0707 10:47:21.735437  3142 layer_factory.hpp:77] Creating layer relu6I0707 10:47:21.735448  3142 net.cpp:84] Creating Layer relu6I0707 10:47:21.735456  3142 net.cpp:406] relu6 <- fc6I0707 10:47:21.735463  3142 net.cpp:367] relu6 -> fc6 (in-place)I0707 10:47:21.736017  3142 net.cpp:122] Setting up relu6I0707 10:47:21.736039  3142 net.cpp:129] Top shape: 1 4096 (4096)I0707 10:47:21.736042  3142 net.cpp:137] Memory required for data: 273914276I0707 10:47:21.736044  3142 layer_factory.hpp:77] Creating layer fc7I0707 10:47:21.736052  3142 net.cpp:84] Creating Layer fc7I0707 10:47:21.736054  3142 net.cpp:406] fc7 <- fc6I0707 10:47:21.736058  3142 net.cpp:380] fc7 -> fc7I0707 10:47:21.766991  3142 net.cpp:122] Setting up fc7I0707 10:47:21.767033  3142 net.cpp:129] Top shape: 1 4096 (4096)I0707 10:47:21.767038  3142 net.cpp:137] Memory required for data: 273930660I0707 10:47:21.767050  3142 layer_factory.hpp:77] Creating layer silence_fc7I0707 10:47:21.767062  3142 net.cpp:84] Creating Layer silence_fc7I0707 10:47:21.767067  3142 net.cpp:406] silence_fc7 <- fc7I0707 10:47:21.767074  3142 net.cpp:122] Setting up silence_fc7I0707 10:47:21.767076  3142 net.cpp:137] Memory required for data: 273930660I0707 10:47:21.767079  3142 net.cpp:200] silence_fc7 does not need backward computation.I0707 10:47:21.767088  3142 net.cpp:200] fc7 does not need backward computation.I0707 10:47:21.767092  3142 net.cpp:200] relu6 does not need backward computation.I0707 10:47:21.767097  3142 net.cpp:200] fc6 does not need backward computation.I0707 10:47:21.767102  3142 net.cpp:200] dummy_roi_pool_conv5 does not need backward computation.I0707 10:47:21.767105  3142 net.cpp:198] rpn_loss_bbox needs backward computation.I0707 10:47:21.767110  3142 net.cpp:198] rpn_loss_cls needs backward computation.I0707 10:47:21.767114  3142 net.cpp:198] rpn-data needs backward computation.I0707 10:47:21.767122  3142 net.cpp:198] rpn_cls_score_reshape needs backward computation.I0707 10:47:21.767144  3142 net.cpp:198] rpn_bbox_pred needs backward computation.I0707 10:47:21.767163  3142 net.cpp:198] rpn_cls_score_rpn_cls_score_0_split needs backward computation.I0707 10:47:21.767176  3142 net.cpp:198] rpn_cls_score needs backward computation.I0707 10:47:21.767191  3142 net.cpp:198] rpn_conv1_rpn_relu1_0_split needs backward computation.I0707 10:47:21.767199  3142 net.cpp:198] rpn_relu1 needs backward computation.I0707 10:47:21.767211  3142 net.cpp:198] rpn_conv1 needs backward computation.I0707 10:47:21.767216  3142 net.cpp:198] relu5 needs backward computation.I0707 10:47:21.767220  3142 net.cpp:198] conv5 needs backward computation.I0707 10:47:21.767225  3142 net.cpp:198] relu4 needs backward computation.I0707 10:47:21.767232  3142 net.cpp:198] conv4 needs backward computation.I0707 10:47:21.767237  3142 net.cpp:198] relu3 needs backward computation.I0707 10:47:21.767241  3142 net.cpp:198] conv3 needs backward computation.I0707 10:47:21.767244  3142 net.cpp:198] pool2 needs backward computation.I0707 10:47:21.767251  3142 net.cpp:198] norm2 needs backward computation.I0707 10:47:21.767254  3142 net.cpp:198] relu2 needs backward computation.I0707 10:47:21.767257  3142 net.cpp:198] conv2 needs backward computation.I0707 10:47:21.767261  3142 net.cpp:198] pool1 needs backward computation.I0707 10:47:21.767264  3142 net.cpp:198] norm1 needs backward computation.I0707 10:47:21.767268  3142 net.cpp:198] relu1 needs backward computation.I0707 10:47:21.767283  3142 net.cpp:198] conv1 needs backward computation.I0707 10:47:21.767297  3142 net.cpp:200] data_input-data_0_split does not need backward computation.I0707 10:47:21.767303  3142 net.cpp:200] input-data does not need backward computation.I0707 10:47:21.767307  3142 net.cpp:242] This network produces output rpn_cls_lossI0707 10:47:21.767313  3142 net.cpp:242] This network produces output rpn_loss_bboxI0707 10:47:21.767336  3142 net.cpp:255] Network initialization done.I0707 10:47:21.767424  3142 solver.cpp:56] Solver scaffolding done.Loading pretrained model weights from data/imagenet_models/ZF.v2.caffemodelI0707 10:47:25.234427  3142 upgrade_proto.cpp:67] Attempting to upgrade input file specified using deprecated input fields: data/imagenet_models/ZF.v2.caffemodelI0707 10:47:25.234450  3142 upgrade_proto.cpp:70] Successfully upgraded file specified using deprecated input fields.W0707 10:47:25.234455  3142 upgrade_proto.cpp:72] Note that future Caffe releases will only support input layers and not input fields.I0707 10:47:25.239126  3142 net.cpp:744] Ignoring source layer pool5_spm6I0707 10:47:25.239156  3142 net.cpp:744] Ignoring source layer pool5_spm6_flattenI0707 10:47:25.284266  3142 net.cpp:744] Ignoring source layer drop6I0707 10:47:25.302134  3142 net.cpp:744] Ignoring source layer relu7I0707 10:47:25.302163  3142 net.cpp:744] Ignoring source layer drop7I0707 10:47:25.302166  3142 net.cpp:744] Ignoring source layer fc8I0707 10:47:25.302170  3142 net.cpp:744] Ignoring source layer probProcess Process-1:Traceback (most recent call last):  File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap    self.run()  File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run    self._target(*self._args, **self._kwargs)  File "./tools/train_faster_rcnn_alt_opt.py", line 129, in train_rpn    max_iters=max_iters)  File "/home/pikachu/dev/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 157, in train_net    pretrained_model=pretrained_model)  File "/home/pikachu/dev/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 51, in __init__    pb2.text_format.Merge(f.read(), self.solver_param)AttributeError: 'module' object has no attribute 'text_format'