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?

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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: ***
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)]] ```
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