faster-rcnn显示 error using fix

恳请各位大神帮忙看看，报错如下, Error using fix Too many input arguments Error in proposal_train(line 86) fix validation data Error in Faster_RCNN_Train.do_proposal_train(line 7) model_stage.output_model_file=proposal_train(conf,dataset,imdb_train,dataset,roidb_train,… Error in script_faster_rcnn__VOC2007_ZF(line 45) model.stae1_rpn =Faster_RCNN_Train.do_proposal_train(conf_proposal,dataset,model,stage1_rpn,opts.do_val);

faster rcnn运行demo出现错误

faster rcnn配置好之后运行 ./tools/demo.py出现如下错误：： Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: Python

win_caffe_py_fast_rcnn训练报错问题。

layer { name: "rpn_bbox_pred" type: "Convolution" bottom: "rpn_conv1Process Process-1: Traceback (most recent call last): File "D:\Anaconda\Anaconda\lib\multiprocessing\process.py", line 267, in _bootstrap self.run() File "D:\Anaconda\Anaconda\lib\multiprocessing\process.py", line 114, in run self._target(*self._args, **self._kwargs) File "D:\py-faster-rcnn\tools\train_faster_rcnn_alt_opt.py", line 129, in train_rpn max_iters=max_iters) File "D:\py-faster-rcnn\tools\..\lib\fast_rcnn\train.py", line 160, in train_net pretrained_model=pretrained_model) File "D:\py-faster-rcnn\tools\..\lib\fast_rcnn\train.py", line 46, in __init__ self.solver = caffe.SGDSolver(solver_prototxt) File "D:\py-faster-rcnn\tools\..\lib\roi_data_layer\layer.py", line 128, in setup top[idx].reshape(1, self._num_classes * 4) IndexError: Index out of range I0415 19:38:05.625026 12668 layer_factory.cpp:58] Creating layer input-data I0415 19:38:05.682178 12668 net.cpp:84] Creating Layer input-data I0415 19:38:05.682178 12668 net.cpp:380] input-data -> data I0415 19:38:05.682178 12668 net.cpp:380] input-data -> im_info I0415 19:38:05.682178 12668 net.cpp:380] input-data -> gt_boxes 然后就卡在这里了。

window faster rcnn error -sed=c99.

F:\Caffe-cp\faster_rcnn\py-faster-rcnn\lib>python setup.py install home = C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5 running install running bdist_egg running egg_info writing fast_rcnn.egg-info\PKG-INFO writing top-level names to fast_rcnn.egg-info\top_level.txt writing dependency_links to fast_rcnn.egg-info\dependency_links.txt reading manifest file 'fast_rcnn.egg-info\SOURCES.txt' writing manifest file 'fast_rcnn.egg-info\SOURCES.txt' installing library code to build\bdist.win-amd64\egg running install_lib running build_ext skipping 'utils\bbox.c' Cython extension (up-to-date) skipping 'nms\cpu_nms.c' Cython extension (up-to-date) skipping 'pycocotools\_mask.c' Cython extension (up-to-date) building 'pycocotools._mask' extension C:\Users\Joker\AppData\Local\Programs\Common\Microsoft\Visual C++ for Python\9.0\VC\Bin\amd64\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -IE:\anaconda2\anaconda\lib\site-packages\numpy\core\include -Ipycocotools -IE:\anaconda2\anaconda\include -IE:\anaconda2\anaconda\PC /Tcpycocotools\maskApi.c /Fobuild\temp.win-amd64-2.7\Release\pycocotools\maskApi.obj -std=c99 cl : Command line warning D9002 : ignoring unknown option '-std=c99' maskApi.c f:\caffe-cp\faster_rcnn\py-faster-rcnn\lib\pycocotools\maskApi.h(8) : fatal error C1083: Cannot open include file: 'stdbool.h': No such file or directory error: command 'C:\\Users\\Joker\\AppData\\Local\\Programs\\Common\\Microsoft\\Visual C++ for Python\\9.0\\VC\\Bin\\amd64\\cl.exe' failed with exit status 2

在windows环境下使用自己的数据集跑faster rcnn遇到'NoneType' object is subscriptable问题（tensorflow）

在尝试根据网上教程用自己的数据集跑faster rcnn的时候，会跳出'NoneType' object is not subscriptable的问题，但查了网上说明并没有明白错误的原因......请问要如何解决？谢谢！ 

faster rcnn训练的时候应该是哪个层出了问题

+ echo Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-04-19_01-16-47 Logging output to experiments/logs/faster_rcnn_alt_opt_ZF_.txt.2017-04-19_01-16-47 + ./tools/train_faster_rcnn_alt_opt.py --gpu 0 --net_name ZF --weights data/imagenet_models/CaffeNet.v2.caffemodel --imdb voc_2007_trainval --cfg experiments/cfgs/faster_rcnn_alt_opt.yml Called with args: Namespace(cfg_file='experiments/cfgs/faster_rcnn_alt_opt.yml', gpu_id=0, imdb_name='voc_2007_trainval', net_name='ZF', pretrained_model='data/imagenet_models/CaffeNet.v2.caffemodel', set_cfgs=None) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Stage 1 RPN, init from ImageNet model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Init model: data/imagenet_models/CaffeNet.v2.caffemodel Using config: {'DATA_DIR': 'E:\\caffe-frcnn\\py-faster-rcnn-master\\data', 'DEDUP_BOXES': 0.0625, 'EPS': 1e-14, 'EXP_DIR': 'default', 'GPU_ID': 0, 'MATLAB': 'matlab', 'MODELS_DIR': 'E:\\caffe-frcnn\\py-faster-rcnn-master\\models\\pascal_voc', 'PIXEL_MEANS': array([[[ 102.9801, 115.9465, 122.7717]]]), 'RNG_SEED': 3, 'ROOT_DIR': 'E:\\caffe-frcnn\\py-faster-rcnn-master', 'TEST': {'BBOX_REG': True, 'HAS_RPN': False, 'MAX_SIZE': 1000, 'NMS': 0.3, 'PROPOSAL_METHOD': 'selective_search', 'RPN_MIN_SIZE': 16, 'RPN_NMS_THRESH': 0.7, 'RPN_POST_NMS_TOP_N': 300, 'RPN_PRE_NMS_TOP_N': 6000, 'SCALES': [600], 'SVM': False}, 'TRAIN': {'ASPECT_GROUPING': True, 'BATCH_SIZE': 128, 'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0], 'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2], 'BBOX_NORMALIZE_TARGETS': True, 'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': False, 'BBOX_REG': False, 'BBOX_THRESH': 0.5, 'BG_THRESH_HI': 0.5, 'BG_THRESH_LO': 0.1, 'FG_FRACTION': 0.25, 'FG_THRESH': 0.5, 'HAS_RPN': True, 'IMS_PER_BATCH': 1, 'MAX_SIZE': 1000, 'PROPOSAL_METHOD': 'gt', 'RPN_BATCHSIZE': 256, 'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'RPN_CLOBBER_POSITIVES': False, 'RPN_FG_FRACTION': 0.5, 'RPN_MIN_SIZE': 16, 'RPN_NEGATIVE_OVERLAP': 0.3, 'RPN_NMS_THRESH': 0.7, 'RPN_POSITIVE_OVERLAP': 0.7, 'RPN_POSITIVE_WEIGHT': -1.0, 'RPN_POST_NMS_TOP_N': 2000, 'RPN_PRE_NMS_TOP_N': 12000, 'SCALES': [600], 'SNAPSHOT_INFIX': '', 'SNAPSHOT_ITERS': 10000, 'USE_FLIPPED': True, 'USE_PREFETCH': False}, 'USE_GPU_NMS': True} Loaded dataset `voc_2007_trainval` for training Set proposal method: gt Appending horizontally-flipped training examples... voc_2007_trainval gt roidb loaded from E:\caffe-frcnn\py-faster-rcnn-master\data\cache\voc_2007_trainval_gt_roidb.pkl done Preparing training data... done roidb len: 100 Output will be saved to `E:\caffe-frcnn\py-faster-rcnn-master\output\default\voc_2007_trainval` Filtered 0 roidb entries: 100 -> 100 WARNING: Logging before InitGoogleLogging() is written to STDERR I0419 01:16:54.964942 25240 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead. I0419 01:16:55.073168 25240 solver.cpp:44] Initializing solver from parameters: train_net: "models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt" base_lr: 0.001 display: 20 lr_policy: "step" gamma: 0.1 momentum: 0.9 weight_decay: 0.0005 stepsize: 60000 snapshot: 0 snapshot_prefix: "zf_rpn" average_loss: 100 I0419 01:16:55.073168 25240 solver.cpp:77] Creating training net from train_net file: models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_rpn_train.pt I0419 01:16:55.074168 25240 net.cpp:51] Initializing net from parameters: name: "ZF" state { phase: TRAIN } layer { name: "input-data" type: "Python" top: "data" top: "im_info" top: "gt_boxes" python_param { module: "roi_data_layer.layer" layer: "RoIDataLayer" param_str: "\'num_classes\': 2" } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 96 pad: 3 kernel_size: 7 stride: 2 } } layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" } layer { name: "norm1" type: "LRN" bottom: "conv1" top: "norm1" lrn_param { local_size: 3 alpha: 5e-05 beta: 0.75 norm_region: WITHIN_CHANNEL engine: CAFFE } } layer { name: "pool1" type: "Pooling" bottom: "norm1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 2 kernel_size: 5 stride: 2 } } layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" } layer { name: "norm2" type: "LRN" bottom: "conv2" top: "norm2" lrn_param { local_size: 3 alpha: 5e-05 beta: 0.75 norm_region: WITHIN_CHANNEL engine: CAFFE } } layer { name: "pool2" type: "Pooling" bottom: "norm2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 pad: 1 } } layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" } layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 384 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5" } layer { name: "rpn_conv1" type: "Convolution" bottom: "conv5" top: "rpn_conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_relu1" type: "ReLU" bottom: "rpn_conv1" top: "rpn_conv1" } layer { name: "rpn_cls_score" type: "Convolution" bottom: "rpn_conv1" top: "rpn_cls_score" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 18 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_bbox_pred" type: "Convolution" bottom: "rpn_conv1"RoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1} top: "rpn_bbox_pred" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 36 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_cls_score_reshape" type: "Reshape" bottom: "rpn_cls_score" top: "rpn_cls_score_reshape" reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } } layer { name: "rpn-data" type: "Python" bottom: "rpn_cls_score" bottom: "gt_boxes" bottom: "im_info" bottom: "data" top: "rpn_labels" top: "rpn_bbox_targets" top: "rpn_bbox_inside_weights" top: "rpn_bbox_outside_weights" python_param { module: "rpn.anchor_target_layer" layer: "AnchorTargetLayer" param_str: "\'feat_stride\': 16" } } layer { name: "rpn_loss_cls" type: "SoftmaxWithLoss" bottom: "rpn_cls_score_reshape" bottom: "rpn_labels" top: "rpn_cls_loss" loss_weight: 1 propagate_down: true propagate_down: false loss_param { ignore_label: -1 normalize: true } } layer { name: "rpn_loss_bbox" type: "SmoothL1Loss" bottom: "rpn_bbox_pred" bottom: "rpn_bbox_targets" bottom: "rpn_bbox_inside_weights" bottom: "rpn_bbox_outside_weights" top: "rpn_loss_bbox" loss_weight: 1 smooth_l1_loss_param { sigma: 3 } } layer { name: "dummy_roi_pool_conv5" type: "DummyData" top: "dummy_roi_pool_conv5" dummy_data_param { data_filler { type: "gaussian" std: 0.01 } shape { dim: 1 dim: 9216 } } } layer { name: "fc6" type: "InnerProduct" bottom: "dummy_roi_pool_conv5" top: "fc6" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } inner_product_param { num_output: 4096 } } layer { name: "silence_fc7" type: "Silence" bottom: "fc7" } I0419 01:16:55.074668 25240 layer_factory.cpp:58] Creating layer input-data I0419 01:16:55.109673 25240 net.cpp:84] Creating Layer input-data I0419 01:16:55.109673 25240 net.cpp:380] input-data -> data I0419 01:16:55.109673 25240 net.cpp:380] input-data -> im_info I0419 01:16:55.109673 25240 net.cpp:380] input-data -> gt_boxes I0419 01:16:55.111171 25240 net.cpp:122] Setting up input-data I0419 01:16:55.111171 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111171 25240 net.cpp:129] Top shape: 1 3 (3) I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 4 (4) I0419 01:16:55.111668 25240 net.cpp:137] Memory required for data: 7200028 I0419 01:16:55.111668 25240 layer_factory.cpp:58] Creating layer data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:84] Creating Layer data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:406] data_input-data_0_split <- data I0419 01:16:55.111668 25240 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_0 I0419 01:16:55.111668 25240 net.cpp:380] data_input-data_0_split -> data_input-data_0_split_1 I0419 01:16:55.111668 25240 net.cpp:122] Setting up data_input-data_0_split I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111668 25240 net.cpp:129] Top shape: 1 3 600 1000 (1800000) I0419 01:16:55.111668 25240 net.cpp:137] Memory required for data: 21600028 I0419 01:16:55.111668 25240 layer_factory.cpp:58] Creating layer conv1 I0419 01:16:55.111668 25240 net.cpp:84] Creating Layer conv1 I0419 01:16:55.111668 25240 net.cpp:406] conv1 <- data_input-data_0_split_0 I0419 01:16:55.111668 25240 net.cpp:380] conv1 -> conv1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up conv1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 79200028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer relu1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer relu1 I0419 01:16:55.577394 25240 net.cpp:406] relu1 <- conv1 I0419 01:16:55.577394 25240 net.cpp:367] relu1 -> conv1 (in-place) I0419 01:16:55.577394 25240 net.cpp:122] Setting up relu1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 136800028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer norm1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer norm1 I0419 01:16:55.577394 25240 net.cpp:406] norm1 <- conv1 I0419 01:16:55.577394 25240 net.cpp:380] norm1 -> norm1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up norm1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 300 500 (14400000) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 194400028 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer pool1 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer pool1 I0419 01:16:55.577394 25240 net.cpp:406] pool1 <- norm1 I0419 01:16:55.577394 25240 net.cpp:380] pool1 -> pool1 I0419 01:16:55.577394 25240 net.cpp:122] Setting up pool1 I0419 01:16:55.577394 25240 net.cpp:129] Top shape: 1 96 151 251 (3638496) I0419 01:16:55.577394 25240 net.cpp:137] Memory required for data: 208954012 I0419 01:16:55.577394 25240 layer_factory.cpp:58] Creating layer conv2 I0419 01:16:55.577394 25240 net.cpp:84] Creating Layer conv2 I0419 01:16:55.577394 25240 net.cpp:406] conv2 <- pool1 I0419 01:16:55.577394 25240 net.cpp:380] conv2 -> conv2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 218759836 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu2 I0419 01:16:55.593016 25240 net.cpp:406] relu2 <- conv2 I0419 01:16:55.593016 25240 net.cpp:367] relu2 -> conv2 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 228565660 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer norm2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer norm2 I0419 01:16:55.593016 25240 net.cpp:406] norm2 <- conv2 I0419 01:16:55.593016 25240 net.cpp:380] norm2 -> norm2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up norm2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 76 126 (2451456) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 238371484 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer pool2 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer pool2 I0419 01:16:55.593016 25240 net.cpp:406] pool2 <- norm2 I0419 01:16:55.593016 25240 net.cpp:380] pool2 -> pool2 I0419 01:16:55.593016 25240 net.cpp:122] Setting up pool2 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 240927388 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv3 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv3 I0419 01:16:55.593016 25240 net.cpp:406] conv3 <- pool2 I0419 01:16:55.593016 25240 net.cpp:380] conv3 -> conv3 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv3 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 244761244 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu3 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu3 I0419 01:16:55.593016 25240 net.cpp:406] relu3 <- conv3 I0419 01:16:55.593016 25240 net.cpp:367] relu3 -> conv3 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu3 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 248595100 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv4 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv4 I0419 01:16:55.593016 25240 net.cpp:406] conv4 <- conv3 I0419 01:16:55.593016 25240 net.cpp:380] conv4 -> conv4 I0419 01:16:55.593016 25240 net.cpp:122] Setting up conv4 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 252428956 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer relu4 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer relu4 I0419 01:16:55.593016 25240 net.cpp:406] relu4 <- conv4 I0419 01:16:55.593016 25240 net.cpp:367] relu4 -> conv4 (in-place) I0419 01:16:55.593016 25240 net.cpp:122] Setting up relu4 I0419 01:16:55.593016 25240 net.cpp:129] Top shape: 1 384 39 64 (958464) I0419 01:16:55.593016 25240 net.cpp:137] Memory required for data: 256262812 I0419 01:16:55.593016 25240 layer_factory.cpp:58] Creating layer conv5 I0419 01:16:55.593016 25240 net.cpp:84] Creating Layer conv5 I0419 01:16:55.593016 25240 net.cpp:406] conv5 <- conv4 I0419 01:16:55.593016 25240 net.cpp:380] conv5 -> conv5 I0419 01:16:55.608644 25240 net.cpp:122] Setting up conv5 I0419 01:16:55.608644 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.608644 25240 net.cpp:137] Memory required for data: 258818716 I0419 01:16:55.608644 25240 layer_factory.cpp:58] Creating layer relu5 I0419 01:16:55.608644 25240 net.cpp:84] Creating Layer relu5 I0419 01:16:55.608644 25240 net.cpp:406] relu5 <- conv5 I0419 01:16:55.608644 25240 net.cpp:367] relu5 -> conv5 (in-place) I0419 01:16:55.608644 25240 net.cpp:122] Setting up relu5 I0419 01:16:55.608644 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.608644 25240 net.cpp:137] Memory required for data: 261374620 I0419 01:16:55.608644 25240 layer_factory.cpp:58] Creating layer rpn_conv1 I0419 01:16:55.608644 25240 net.cpp:84] Creating Layer rpn_conv1 I0419 01:16:55.608644 25240 net.cpp:406] rpn_conv1 <- conv5 I0419 01:16:55.608644 25240 net.cpp:380] rpn_conv1 -> rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 263930524 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:406] rpn_relu1 <- rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:367] rpn_relu1 -> rpn_conv1 (in-place) I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_relu1 I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 266486428 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:406] rpn_conv1_rpn_relu1_0_split <- rpn_conv1 I0419 01:16:55.624267 25240 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_conv1_rpn_relu1_0_split -> rpn_conv1_rpn_relu1_0_split_1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_conv1_rpn_relu1_0_split I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 256 39 64 (638976) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 271598236 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score <- rpn_conv1_rpn_relu1_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score -> rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 271777948 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1 I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score_rpn_cls_score_0_split I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 18 39 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272137372 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:406] rpn_bbox_pred <- rpn_conv1_rpn_relu1_0_split_1 I0419 01:16:55.624267 25240 net.cpp:380] rpn_bbox_pred -> rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_bbox_pred I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272496796 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:84] Creating Layer rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:406] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0 I0419 01:16:55.624267 25240 net.cpp:380] rpn_cls_score_reshape -> rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:122] Setting up rpn_cls_score_reshape I0419 01:16:55.624267 25240 net.cpp:129] Top shape: 1 2 351 64 (44928) I0419 01:16:55.624267 25240 net.cpp:137] Memory required for data: 272676508 I0419 01:16:55.624267 25240 layer_factory.cpp:58] Creating layer rpn-data I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn-data I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1 I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- gt_boxes I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- im_info I0419 01:16:55.639891 25240 net.cpp:406] rpn-data <- data_input-data_0_split_1 I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_labels I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_targets I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_inside_weights I0419 01:16:55.639891 25240 net.cpp:380] rpn-data -> rpn_bbox_outside_weights I0419 01:16:55.639891 25240 net.cpp:122] Setting up rpn-data I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 1 351 64 (22464) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:129] Top shape: 1 36 39 64 (89856) I0419 01:16:55.639891 25240 net.cpp:137] Memory required for data: 273844636 I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_cls <- rpn_cls_score_reshape I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_cls <- rpn_labels I0419 01:16:55.639891 25240 net.cpp:380] rpn_loss_cls -> rpn_cls_loss I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:122] Setting up rpn_loss_cls I0419 01:16:55.639891 25240 net.cpp:129] Top shape: (1) I0419 01:16:55.639891 25240 net.cpp:132] with loss weight 1 I0419 01:16:55.639891 25240 net.cpp:137] Memory required for data: 273844640 I0419 01:16:55.639891 25240 layer_factory.cpp:58] Creating layer rpn_loss_bbox I0419 01:16:55.639891 25240 net.cpp:84] Creating Layer rpn_loss_bbox I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_bbox <- rpn_bbox_pred I0419 01:16:55.639891 25240 net.cpp:406] rpn_loss_bbox <- rpn_bbox_targets I0419 01:16:55.639891 2*** Check failure stack trace: ***

Faster-RCNN-TensorFlow-Python3-master训练后，如何得到AP，mAP的结果

查了很多资料，tf-faster-rcnn和caffe-faster-rcnn里都是用test__net.py 来评估训练结果。但是我用的是Faster-RCNN-TensorFlow-Python3-master，里面没有test_net.py。那要怎么获得AP和mAP的结果呢？

faster rcnn执行代码中的fix layers 是什么意思？

 如图，restored的不应该是通过预训练后得到的参数吗？为什么冒号后面是0？？还有最后的Fix是什么意思？意思是这几层在faster rcnn 训练时参数都不变了吗？

ubuntu跑faster_rcnn的demo不出界面

ubuntu跑faster_rcnn的demo不出界面，但是也没有报错，就是不出界面是怎么回事？ ubuntu 16 cuda 8.0

faster-rcnn 关于预训练的问题

faster -rcnn 训练分成两步： 1. per-train 采用Image Net的数据集（1000类，一千万张图片） 2. fine-tuning 采用pascal voc _ 2007 （20类，一万张图片） 或其他数据集 问题： 如果我想训练另外一个数据集，例如做细胞检测，大概有三类，可不可以直接用第一步per-train的模型来进行初始化参数？如果不可以，大概需要多少张细胞图像做预训练？

faster-RCNN 分类层

请问RPN分类层cls_score为什么要输出两个参数:每个anchor的前景概率和背景概率？背景概率不就等于（1－前景概率）吗？

如何可视化tensorflow版的fater rcnn的训练过程？

小白一枚，github下的faster rcnn tf版在win10系统上，代码里没有输出训练日志的语句无log文件， 想问一下怎么添加语句可以通过tensorboard显示？下为train代码 import time import tensorflow as tf import numpy as np from tensorflow.python import pywrap_tensorflow import lib.config.config as cfg from lib.datasets import roidb as rdl_roidb from lib.datasets.factory import get_imdb from lib.datasets.imdb import imdb as imdb2 from lib.layer_utils.roi_data_layer import RoIDataLayer from lib.nets.vgg16 import vgg16 from lib.utils.timer import Timer try: import cPickle as pickle except ImportError: import pickle import os def get_training_roidb(imdb): """Returns a roidb (Region of Interest database) for use in training.""" if True: print('Appending horizontally-flipped training examples...') imdb.append_flipped_images() print('done') print('Preparing training data...') rdl_roidb.prepare_roidb(imdb) print('done') return imdb.roidb def combined_roidb(imdb_names): """ Combine multiple roidbs """ def get_roidb(imdb_name): imdb = get_imdb(imdb_name) print('Loaded dataset `{:s}` for training'.format(imdb.name)) imdb.set_proposal_method("gt") print('Set proposal method: {:s}'.format("gt")) roidb = get_training_roidb(imdb) return roidb roidbs = [get_roidb(s) for s in imdb_names.split('+')] roidb = roidbs[0] if len(roidbs) > 1: for r in roidbs[1:]: roidb.extend(r) tmp = get_imdb(imdb_names.split('+')[1]) imdb = imdb2(imdb_names, tmp.classes) else: imdb = get_imdb(imdb_names) return imdb, roidb class Train: def __init__(self): # Create network if cfg.FLAGS.net == 'vgg16': self.net = vgg16(batch_size=cfg.FLAGS.ims_per_batch) else: raise NotImplementedError self.imdb, self.roidb = combined_roidb("voc_2007_trainval") self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes) self.output_dir = cfg.get_output_dir(self.imdb, 'default') def train(self): # Create session tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True sess = tf.Session(config=tfconfig) with sess.graph.as_default(): tf.set_random_seed(cfg.FLAGS.rng_seed) layers = self.net.create_architecture(sess, "TRAIN", self.imdb.num_classes, tag='default') loss = layers['total_loss'] lr = tf.Variable(cfg.FLAGS.learning_rate, trainable=False) momentum = cfg.FLAGS.momentum optimizer = tf.train.MomentumOptimizer(lr, momentum) gvs = optimizer.compute_gradients(loss) # Double bias # Double the gradient of the bias if set if cfg.FLAGS.double_bias: final_gvs = [] with tf.variable_scope('Gradient_Mult'): for grad, var in gvs: scale = 1. if cfg.FLAGS.double_bias and '/biases:' in var.name: scale *= 2. if not np.allclose(scale, 1.0): grad = tf.multiply(grad, scale) final_gvs.append((grad, var)) train_op = optimizer.apply_gradients(final_gvs) else: train_op = optimizer.apply_gradients(gvs) # We will handle the snapshots ourselves self.saver = tf.train.Saver(max_to_keep=100000) # Write the train and validation information to tensorboard # writer = tf.summary.FileWriter(self.tbdir, sess.graph) # valwriter = tf.summary.FileWriter(self.tbvaldir) # Load weights # Fresh train directly from ImageNet weights print('Loading initial model weights from {:s}'.format(cfg.FLAGS.pretrained_model)) variables = tf.global_variables() # Initialize all variables first sess.run(tf.variables_initializer(variables, name='init')) var_keep_dic = self.get_variables_in_checkpoint_file(cfg.FLAGS.pretrained_model) # Get the variables to restore, ignorizing the variables to fix variables_to_restore = self.net.get_variables_to_restore(variables, var_keep_dic) restorer = tf.train.Saver(variables_to_restore) restorer.restore(sess, cfg.FLAGS.pretrained_model) print('Loaded.') # Need to fix the variables before loading, so that the RGB weights are changed to BGR # For VGG16 it also changes the convolutional weights fc6 and fc7 to # fully connected weights self.net.fix_variables(sess, cfg.FLAGS.pretrained_model) print('Fixed.') sess.run(tf.assign(lr, cfg.FLAGS.learning_rate)) last_snapshot_iter = 0 timer = Timer() iter = last_snapshot_iter + 1 last_summary_time = time.time() while iter < cfg.FLAGS.max_iters + 1: # Learning rate if iter == cfg.FLAGS.step_size + 1: # Add snapshot here before reducing the learning rate # self.snapshot(sess, iter) sess.run(tf.assign(lr, cfg.FLAGS.learning_rate * cfg.FLAGS.gamma)) timer.tic() # Get training data, one batch at a time blobs = self.data_layer.forward() # Compute the graph without summary rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss = self.net.train_step(sess, blobs, train_op) timer.toc() iter += 1 # Display training information if iter % (cfg.FLAGS.display) == 0: print('iter: %d / %d, total loss: %.6f\n >>> rpn_loss_cls: %.6f\n ' '>>> rpn_loss_box: %.6f\n >>> loss_cls: %.6f\n >>> loss_box: %.6f\n ' % \ (iter, cfg.FLAGS.max_iters, total_loss, rpn_loss_cls, rpn_loss_box, loss_cls, loss_box)) print('speed: {:.3f}s / iter'.format(timer.average_time)) if iter % cfg.FLAGS.snapshot_iterations == 0: self.snapshot(sess, iter ) def get_variables_in_checkpoint_file(self, file_name): try: reader = pywrap_tensorflow.NewCheckpointReader(file_name) var_to_shape_map = reader.get_variable_to_shape_map() return var_to_shape_map except Exception as e: # pylint: disable=broad-except print(str(e)) if "corrupted compressed block contents" in str(e): print("It's likely that your checkpoint file has been compressed " "with SNAPPY.") def snapshot(self, sess, iter): net = self.net if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) # Store the model snapshot filename = 'vgg16_faster_rcnn_iter_{:d}'.format(iter) + '.ckpt' filename = os.path.join(self.output_dir, filename) self.saver.save(sess, filename) print('Wrote snapshot to: {:s}'.format(filename)) # Also store some meta information, random state, etc. nfilename = 'vgg16_faster_rcnn_iter_{:d}'.format(iter) + '.pkl' nfilename = os.path.join(self.output_dir, nfilename) # current state of numpy random st0 = np.random.get_state() # current position in the database cur = self.data_layer._cur # current shuffled indeces of the database perm = self.data_layer._perm # Dump the meta info with open(nfilename, 'wb') as fid: pickle.dump(st0, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(cur, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(perm, fid, pickle.HIGHEST_PROTOCOL) pickle.dump(iter, fid, pickle.HIGHEST_PROTOCOL) return filename, nfilename if __name__ == '__main__': train = Train() train.train()

faster rcnn训练自己的数据集 测试结果一片红色

搞了好几天才训练出来的网络，测试时输入是黑白图片，结果是一片红色，但是标注的位置大致是对的，请问这是怎么回事？是图片的问题吗？

slim微调后的模型可以用在tf-faster rcnn上进行细粒度测试吗？

这是用在tf-faster rcnn上的错误 ``` Traceback (most recent call last): File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1322, in _do_call return fn(*args) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1307, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1409, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.NotFoundError: Key resnet_v1_101/bbox_pred/biases not found in checkpoint [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "../tools/demo.py", line 189, in <module> print(saver.restore(sess,tfmodel)) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1768, in restore six.reraise(exception_type, exception_value, exception_traceback) File "/home/lf/anaconda3/lib/python3.6/site-packages/six.py", line 693, in reraise raise value File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1752, in restore {self.saver_def.filename_tensor_name: save_path}) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 900, in run run_metadata_ptr) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1135, in _run feed_dict_tensor, options, run_metadata) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1316, in _do_run run_metadata) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1335, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.NotFoundError: Key resnet_v1_101/bbox_pred/biases not found in checkpoint [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]] Caused by op 'save/RestoreV2', defined at: File "../tools/demo.py", line 187, in <module> saver = tf.train.Saver() File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1284, in __init__ self.build() File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1296, in build self._build(self._filename, build_save=True, build_restore=True) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1333, in _build build_save=build_save, build_restore=build_restore) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 781, in _build_internal restore_sequentially, reshape) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 400, in _AddRestoreOps restore_sequentially) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 832, in bulk_restore return io_ops.restore_v2(filename_tensor, names, slices, dtypes) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_io_ops.py", line 1463, in restore_v2 shape_and_slices=shape_and_slices, dtypes=dtypes, name=name) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3414, in create_op op_def=op_def) File "/home/lf/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1740, in __init__ self._traceback = self._graph._extract_stack() # pylint: disable=protected-access NotFoundError (see above for traceback): Key resnet_v1_101/bbox_pred/biases not found in checkpoint [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]] ```

在win10系统中使用python运行faster-rcnn训练自己的数据集出现问题

在win10系统中使用python运行faster-rcnn训练自己的数据集出现以下问题： I0417 16:38:45.682274 7396 layer_factory.hpp:77] Creating layer rpn_cls_score_rpn_cls_score*** Check failure stack trace: *** 请问该问题产生的原因有可能是哪些？应该怎么解决？

Network 是怎么编写的

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用faster rcnn来训练自己的数据集，在进行配置接口时出现如下错误

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