用create_pascal_tf_record.py时候出现的问题! 40C

这是我用create_pascal_tf_record.py出现的错误

D:\tensorflow\models\research\object_detection>python dataset_tools\create_pascal_tf_record.py --label_map=D:\tensorflow\pedestrain_train\data\label_map.pbtxt --data_dir=D:\pedestrain_data --year=VOC2012 --set=train --output_path=D:\pascal_train.record
Traceback (most recent call last):
  File "dataset_tools\create_pascal_tf_record.py", line 185, in <module>
    tf.app.run()
  File "C:\anaconda\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
    _sys.exit(main(argv))
  File "dataset_tools\create_pascal_tf_record.py", line 167, in main
    examples_list = dataset_util.read_examples_list(examples_path)
  File "D:\ssd-detection\models-master\research\object_detection\utils\dataset_util.py", line 59, in read_examples_list
    lines = fid.readlines()
  File "C:\anaconda\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 188, in readlines
    self._preread_check()
  File "C:\anaconda\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 85, in _preread_check
    compat.as_bytes(self.__name), 1024 * 512, status)
  File "C:\anaconda\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 519, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.NotFoundError: NewRandomAccessFile failed to Create/Open: D:\pedestrain_data\VOC2012\ImageSets\Main\aeroplane_train.txt : \u03f5\u0373\udcd5\u04b2\udcbb\udcb5\udcbd\u05b8\udcb6\udca8\udcb5\udcc4\udcce\u013c\udcfe\udca1\udca3
; No such file or directory

可是我的main文件夹里面是pedestrain_train.txt和pedestrain_val.txt为什么他要去找aeroplane_train.txt这个文件呢

1个回答

你搜索下dataset_util.py这个文件,里面肯定有aeroplane_train.txt的逻辑

caozhy
贵阳老马马善福专业维修游泳池堵漏防水工程 回复pclose: 不是,你可以定义成变量或者函数的参数
8 个月之前 回复
qq_43068488
pclose 我看了下create_pascal_tr_record.py的源码,在第165行的地方,是aeroplane,我把它改成了pedstrian,才解决的,那我想问下以后这个文件名是不是只能固定了?
8 个月之前 回复
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I0419 01:16:55.073168 25240 solver.cpp:44] Initializing solver from parameters: train_net: "models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt" base_lr: 0.001 display: 20 lr_policy: "step" gamma: 0.1 momentum: 0.9 weight_decay: 0.0005 stepsize: 60000 snapshot: 0 snapshot_prefix: "zf_rpn" average_loss: 100 I0419 01:16:55.073168 25240 solver.cpp:77] Creating training net from train_net file: models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt I0419 01:16:55.074168 25240 net.cpp:51] Initializing net from parameters: name: "ZF" state { phase: TRAIN } layer { name: "input-data" type: "Python" top: "data" top: "im_info" top: "gt_boxes" python_param { module: "roi_data_layer.layer" layer: "RoIDataLayer" param_str: "\'num_classes\': 2" } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 96 pad: 3 kernel_size: 7 stride: 2 } } layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" } layer { name: "norm1" type: "LRN" bottom: "conv1" top: "norm1" lrn_param { local_size: 3 alpha: 5e-05 beta: 0.75 norm_region: WITHIN_CHANNEL engine: CAFFE } } layer { name: "pool1" type: "Pooling" bottom: "norm1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 2 kernel_size: 5 stride: 2 } } layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" } layer { name: "norm2" type: "LRN" bottom: "conv2" top: "norm2" lrn_param { local_size: 3 alpha: 5e-05 beta: 0.75 norm_region: WITHIN_CHANNEL engine: CAFFE } } layer { name: "pool2" type: "Pooling" bottom: "norm2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } } layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" } layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5" } layer { name: "rpn_conv1" type: "Convolution" bottom: "conv5" top: "rpn_conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_relu1" type: "ReLU" bottom: "rpn_conv1" top: "rpn_conv1" } layer { name: "rpn_cls_score" type: "Convolution" bottom: "rpn_conv1" top: "rpn_cls_score" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 18 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_bbox_pred" type: "Convolution" bottom: "rpn_conv1"RoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1} top: "rpn_bbox_pred" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 36 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_cls_score_reshape" type: "Reshape" bottom: "rpn_cls_score" top: "rpn_cls_score_reshape" reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } } layer { name: "rpn-data" type: "Python" bottom: "rpn_cls_score" bottom: "gt_boxes" bottom: "im_info" bottom: "data" top: "rpn_labels" top: "rpn_bbox_targets" top: "rpn_bbox_inside_weights" top: "rpn_bbox_outside_weights" python_param { module: "rpn.anchor_target_layer" layer: "AnchorTargetLayer" param_str: "\'feat_stride\': 16" } } layer { name: "rpn_loss_cls" type: "SoftmaxWithLoss" bottom: "rpn_cls_score_reshape" bottom: "rpn_labels" top: "rpn_cls_loss" loss_weight: 1 propagate_down: true propagate_down: false loss_param { ignore_label: -1 normalize: true } } layer { name: "rpn_loss_bbox" type: "SmoothL1Loss" bottom: "rpn_bbox_pred" bottom: "rpn_bbox_targets" bottom: "rpn_bbox_inside_weights" bottom: "rpn_bbox_outside_weights" top: "rpn_loss_bbox" loss_weight: 1 smooth_l1_loss_param { sigma: 3 } } layer { name: "dummy_roi_pool_conv5" type: "DummyData" top: "dummy_roi_pool_conv5" dummy_data_param { data_filler { type: "gaussian" std: 0.01 } shape { dim: 1 dim: 9216 } } } layer { name: "fc6" type: "InnerProduct" bottom: "dummy_roi_pool_conv5" top: "fc6" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } inner_product_param { num_output: 4096 } } layer { name: "silence_fc7" type: "Silence" bottom: "fc7" } I0419 01:16:55.074668 25240 layer_factory.cpp:58] Creating layer input-data I0419 01:16:55.109673 25240 net.cpp:84] Creating Layer input-data I0419 01:16:55.109673 25240 net.cpp:380] input-data -> data I0419 01:16:55.109673 25240 net.cpp:380] input-data -> im_info I0419 01:16:55.109673 25240 net.cpp:380] input-data -> gt_boxes I0419 01:16:55.111171 25240 net.cpp:122] Setting up input-data I0419 01:16:55.111171 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111171 25240 net.cpp:129] Top shape: 1 3 (3) I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 4 (4) I0419 01:16:55.111668 25240 net.cpp:137] Memory required for data: 7200028 I0419 01:16:55.111668 25240 layer_factory.cpp:58] Creating layer data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:84] Creating Layer data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:406] data_input-data_0_split <- data I0419 01:16:55.111668 25240 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_0 I0419 01:16:55.111668 25240 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_1 I0419 01:16:55.111668 25240 net.cpp:122] Setting up data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111668 25240 net.cpp:137] Memory required for data: 21600028 I0419 01:16:55.111668 25240 layer_factory.cpp:58] Creating layer conv1 I0419 01:16:55.111668 25240 net.cpp:84] Creating Layer conv1 I0419 01:16:55.111668 25240 net.cpp:406] conv1 <- data_input-data_0_split_0 I0419 01:16:55.111668 25240 net.cpp:380] conv1 -> conv1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up conv1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 79200028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer relu1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer relu1 I0419 01:16:55.577394 25240 net.cpp:406] relu1 <- conv1 I0419 01:16:55.577394 25240 net.cpp:367] relu1 -> conv1 (in-place) I0419 01:16:55.577394 25240 net.cpp:122] Setting up relu1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 136800028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer norm1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer norm1 I0419 01:16:55.577394 25240 net.cpp:406] norm1 <- conv1 I0419 01:16:55.577394 25240 net.cpp:380] norm1 -> norm1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up norm1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 194400028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer pool1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer pool1 I0419 01:16:55.577394 25240 net.cpp:406] pool1 <- norm1 I0419 01:16:55.577394 25240 net.cpp:380] pool1 -> pool1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up pool1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 151 251 (3638496) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 208954012 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer conv2 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer conv2 I0419 01:16:55.577394 25240 net.cpp:406] conv2 <- pool1 I0419 01:16:55.577394 25240 net.cpp:380] conv2 -> conv2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 218759836 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu2 I0419 01:16:55.593016 25240 net.cpp:406] relu2 <- conv2 I0419 01:16:55.593016 25240 net.cpp:367] relu2 -> conv2 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 228565660 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer norm2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer norm2 I0419 01:16:55.593016 25240 net.cpp:406] norm2 <- conv2 I0419 01:16:55.593016 25240 net.cpp:380] norm2 -> norm2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up norm2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 238371484 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer pool2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer pool2 I0419 01:16:55.593016 25240 net.cpp:406] pool2 <- norm2 I0419 01:16:55.593016 25240 net.cpp:380] pool2 -> pool2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up pool2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 240927388 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv3 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv3 I0419 01:16:55.593016 25240 net.cpp:406] conv3 <- pool2 I0419 01:16:55.593016 25240 net.cpp:380] conv3 -> conv3 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv3 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 244761244 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu3 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu3 I0419 01:16:55.593016 25240 net.cpp:406] relu3 <- conv3 I0419 01:16:55.593016 25240 net.cpp:367] relu3 -> conv3 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu3 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 248595100 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv4 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv4 I0419 01:16:55.593016 25240 net.cpp:406] conv4 <- conv3 I0419 01:16:55.593016 25240 net.cpp:380] conv4 -> conv4 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv4 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 252428956 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu4 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu4 I0419 01:16:55.593016 25240 net.cpp:406] relu4 <- conv4 I0419 01:16:55.593016 25240 net.cpp:367] relu4 -> conv4 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu4 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 256262812 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv5 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv5 I0419 01:16:55.593016 25240 net.cpp:406] conv5 <- conv4 I0419 01:16:55.593016 25240 net.cpp:380] conv5 -> conv5 I0419 01:16:55.608644 25240 net.cpp:122] Setting up conv5 I0419 01:16:55.608644 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.608644 25240 net.cpp:137] Memory required for data: 258818716 I0419 01:16:55.608644 25240 layer_factory.cpp:58] Creating layer relu5 I0419 01:16:55.608644 25240 net.cpp:84] Creating Layer relu5 I0419 01:16:55.608644 25240 net.cpp:406] relu5 <- conv5 I0419 01:16:55.608644 25240 net.cpp:367] relu5 -> conv5 (in-place) I0419 01:16:55.608644 25240 net.cpp:122] Setting up relu5 I0419 01:16:55.608644 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.608644 25240 net.cpp:137] Memory required for data: 261374620 I0419 01:16:55.608644 25240 layer_factory.cpp:58] Creating layer rpn_conv1 I0419 01:16:55.608644 25240 net.cpp:84] Creating Layer rpn_conv1 I0419 01:16:55.608644 25240 net.cpp:406] rpn_conv1 <- conv5 I0419 01:16:55.608644 25240 net.cpp:380] rpn_conv1 -> rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 263930524 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:406] rpn_relu1 <- rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:367] rpn_relu1 -> rpn_conv1 (in-place) I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 266486428 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:406] rpn_conv1_rpn_relu1_0_split <- rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 271598236 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score <- rpn_conv1_rpn_relu1_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score -> rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 271777948 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272137372 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:406] rpn_bbox_pred <- rpn_conv1_rpn_relu1_0_split_1 I0419 01:16:55.624267 25240 net.cpp:380] rpn_bbox_pred -> rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272496796 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_reshape -> rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 2 351 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272676508 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn-data I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn-data I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1 I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- gt_boxes I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- im_info I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- data_input-data_0_split_1 I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_labels I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_targets I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_inside_weights I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_outside_weights I0419 01:16:55.639891 25240 net.cpp:122] Setting up rpn-data I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 1 351 64 (22464) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:137] Memory required for data: 273844636 I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_cls <- rpn_cls_score_reshape I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_cls <- rpn_labels I0419 01:16:55.639891 25240 net.cpp:380] rpn_loss_cls -> rpn_cls_loss I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:122] Setting up rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:129] Top shape: (1) I0419 01:16:55.639891 25240 net.cpp:132] with loss weight 1 I0419 01:16:55.639891 25240 net.cpp:137] Memory required for data: 273844640 I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_bbox I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn_loss_bbox I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_bbox <- rpn_bbox_pred I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_bbox <- rpn_bbox_targets I0419 01:16:55.639891 2*** Check failure stack trace: ***
Pascal菜鸟求帮助!!
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