Comments (3)
mjq11302010044
commented on September 16, 2024
Do you compile the project with cudnn?
from rrpn.
wzhiyuan2016
commented on September 16, 2024
yes, This is my running log file:
/home/x306/wzy/RRPN/tools/train_net.py --gpu 0 --solver /home/x306/wzy/RRPN/models/rrpn/ZF/faster_rcnn_end2end/solver.prototxt
--weights /home/x306/wzy/py-faster-rcnn/data/imagenet_models/ZF.v2.caffemodel --imdb MSRA_TRAIN --iters 1000 --cfg /home/x306/wzy/RRPN/experiments/cfgs/faster_rcnn_end2end.yml
Called with args:
Namespace(cfg_file='/home/x306/wzy/RRPN/experiments/cfgs/faster_rcnn_end2end.yml', gpu_id=0, imdb_name='MSRA_TRAIN', max_iters=1000, pretrained_model='/home/x306/wzy/py-faster-rcnn/data/imagenet_models/ZF.v2.caffemodel', randomize=False, set_cfgs=None, solver='/home/x306/wzy/RRPN/models/rrpn/ZF/faster_rcnn_end2end/solver.prototxt')
Using config:
{'DATA_DIR': '/home/x306/wzy/RRPN/data',
'DEDUP_BOXES': 0.0625,
'EPS': 1e-14,
'EXP_DIR': 'faster_rcnn_end2end',
'GPU_ID': 0,
'IMG_PADDING': 0.25,
'MATLAB': 'matlab',
'MODELS_DIR': '/home/x306/wzy/RRPN/models/pascal_voc',
'PIXEL_MEANS': array([[[ 102.9801, 115.9465, 122.7717]]]),
'RNG_SEED': 3,
'ROOT_DIR': '/home/x306/wzy/RRPN',
'RRPN_MODELS_DIR': '/home/x306/wzy/RRPN/models/rrpn',
'TEST': {'ANGLE_GROUP': [],
'BBOX_REG': True,
'GT_MARGIN': 1.4,
'HAS_RPN': True,
'MAX_SIZE': 1700,
'NMS': 0.3,
'PROPOSAL_METHOD': 'selective_search',
'RATIO_GROUP': [0.2, 0.5, 1.0],
'RPN_MIN_SIZE': 16,
'RPN_NMS_THRESH': 0.7,
'RPN_POST_NMS_TOP_N': 300,
'RPN_PRE_NMS_TOP_N': 6000,
'SCALES': [1000],
'SCALE_GROUP': [],
'SVM': False},
'TRAIN': {'ASPECT_GROUPING': True,
'BATCH_SIZE': 64,
'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': True,
'BBOX_REG': True,
'BBOX_THRESH': 0.5,
'BG_THRESH_HI': 0.5,
'BG_THRESH_LO': 0.0,
'FG_FRACTION': 0.25,
'FG_THRESH': 0.5,
'GT_MARGIN': 1.4,
'HAS_RPN': True,
'IMS_PER_BATCH': 1,
'MAX_SIZE': 1000,
'PROPOSAL_METHOD': 'gt',
'RBBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0, 1.0],
'RBBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0, 0.0],
'RBBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2, 1],
'RBBOX_NORMALIZE_TARGETS_PRECOMPUTED': True,
'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,
'RPN_RBBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0, 1.0],
'R_NEGATIVE_ANGLE_FILTER': 15,
'R_POSITIVE_ANGLE_FILTER': 15,
'SCALES': [600],
'SNAPSHOT_INFIX': '',
'SNAPSHOT_ITERS': 10000,
'USE_FLIPPED': False,
'USE_PREFETCH': False},
'USE_GPU_NMS': True}
MSRA_TRAIN
1 roidb entries
Output will be saved to .
Filtered 0 roidb entries: 1 -> 1
Computing bounding-box regression targets...
rtrain中的SolverWrapper类Computing bounding-box regression targets...
调用了rbbox中的 add_rbbox_regression_target函数
调用了rbbox_transform中的rbbox_transform
bbox target means:
[[ 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0.]]
[ 0. 0. 0. 0. 0.]
bbox target stdevs:
[[ 0.1 0.1 0.2 0.2 1. ]
[ 0.1 0.1 0.2 0.2 1. ]]
[ 0.1 0.1 0.2 0.2 1. ]
Normalizing targets
done
RoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1}
调用了rlayer中的 rRoIDataLaye Setup完成
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0130 16:30:48.655997 18755 solver.cpp:48] Initializing solver from parameters:
train_net: "/home/x306/wzy/RRPN/models/rrpn/ZF/faster_rcnn_end2end/train.prototxt"
base_lr: 0.001
display: 20
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 50000
snapshot: 0
snapshot_prefix: "zf_faster_rcnn"
average_loss: 100
iter_size: 4
I0130 16:30:48.656019 18755 solver.cpp:81] Creating training net from train_net file: /home/x306/wzy/RRPN/models/rrpn/ZF/faster_rcnn_end2end/train.prototxt
I0130 16:30:48.656664 18755 net.cpp:49] Initializing net from parameters:
name: "ZF"
I0130 16:30:48.656795 18755 layer_factory.hpp:77] Creating layer input-data
I0130 16:30:48.657094 18755 net.cpp:106] Creating Layer input-data
I0130 16:30:48.657101 18755 net.cpp:411] input-data -> data
I0130 16:30:48.657109 18755 net.cpp:411] input-data -> im_info
I0130 16:30:48.657112 18755 net.cpp:411] input-data -> gt_boxes
I0130 16:30:48.658023 18755 net.cpp:150] Setting up input-data
I0130 16:30:48.658031 18755 net.cpp:157] Top shape: 1 3 600 1000 (1800000)
I0130 16:30:48.658035 18755 net.cpp:157] Top shape: 1 3 (3)
I0130 16:30:48.658038 18755 net.cpp:157] Top shape: 1 5 (5)
I0130 16:30:48.658040 18755 net.cpp:165] Memory required for data: 7200032
I0130 16:30:48.658042 18755 layer_factory.hpp:77] Creating layer data_input-data_0_split
I0130 16:30:48.658047 18755 net.cpp:106] Creating Layer data_input-data_0_split
I0130 16:30:48.658049 18755 net.cpp:454] data_input-data_0_split <- data
I0130 16:30:48.658053 18755 net.cpp:411] data_input-data_0_split -> data_input-data_0_split_0
I0130 16:30:48.658058 18755 net.cpp:411] data_input-data_0_split -> data_input-data_0_split_1
I0130 16:30:48.658077 18755 net.cpp:150] Setting up data_input-data_0_split
I0130 16:30:48.658082 18755 net.cpp:157] Top shape: 1 3 600 1000 (1800000)
I0130 16:30:48.658084 18755 net.cpp:157] Top shape: 1 3 600 1000 (1800000)
I0130 16:30:48.658087 18755 net.cpp:165] Memory required for data: 21600032
I0130 16:30:48.658087 18755 layer_factory.hpp:77] Creating layer im_info_input-data_1_split
I0130 16:30:48.658092 18755 net.cpp:106] Creating Layer im_info_input-data_1_split
I0130 16:30:48.658093 18755 net.cpp:454] im_info_input-data_1_split <- im_info
I0130 16:30:48.658097 18755 net.cpp:411] im_info_input-data_1_split -> im_info_input-data_1_split_0
I0130 16:30:48.658099 18755 net.cpp:411] im_info_input-data_1_split -> im_info_input-data_1_split_1
I0130 16:30:48.658102 18755 net.cpp:411] im_info_input-data_1_split -> im_info_input-data_1_split_2
I0130 16:30:48.658124 18755 net.cpp:150] Setting up im_info_input-data_1_split
I0130 16:30:48.658128 18755 net.cpp:157] Top shape: 1 3 (3)
I0130 16:30:48.658129 18755 net.cpp:157] Top shape: 1 3 (3)
I0130 16:30:48.658133 18755 net.cpp:157] Top shape: 1 3 (3)
I0130 16:30:48.658133 18755 net.cpp:165] Memory required for data: 21600068
I0130 16:30:48.658135 18755 layer_factory.hpp:77] Creating layer gt_boxes_input-data_2_split
I0130 16:30:48.658138 18755 net.cpp:106] Creating Layer gt_boxes_input-data_2_split
I0130 16:30:48.658140 18755 net.cpp:454] gt_boxes_input-data_2_split <- gt_boxes
I0130 16:30:48.658143 18755 net.cpp:411] gt_boxes_input-data_2_split -> gt_boxes_input-data_2_split_0
I0130 16:30:48.658148 18755 net.cpp:411] gt_boxes_input-data_2_split -> gt_boxes_input-data_2_split_1
I0130 16:30:48.658162 18755 net.cpp:150] Setting up gt_boxes_input-data_2_split
I0130 16:30:48.658165 18755 net.cpp:157] Top shape: 1 5 (5)
I0130 16:30:48.658167 18755 net.cpp:157] Top shape: 1 5 (5)
I0130 16:30:48.658169 18755 net.cpp:165] Memory required for data: 21600108
I0130 16:30:48.658171 18755 layer_factory.hpp:77] Creating layer conv1
I0130 16:30:48.658176 18755 net.cpp:106] Creating Layer conv1
I0130 16:30:48.658179 18755 net.cpp:454] conv1 <- data_input-data_0_split_0
I0130 16:30:48.658182 18755 net.cpp:411] conv1 -> conv1
I0130 16:30:48.850282 18755 net.cpp:150] Setting up conv1
I0130 16:30:48.850301 18755 net.cpp:157] Top shape: 1 96 300 500 (14400000)
I0130 16:30:48.850303 18755 net.cpp:165] Memory required for data: 79200108
I0130 16:30:48.850316 18755 layer_factory.hpp:77] Creating layer relu1
I0130 16:30:48.850327 18755 net.cpp:106] Creating Layer relu1
I0130 16:30:48.850330 18755 net.cpp:454] relu1 <- conv1
I0130 16:30:48.850337 18755 net.cpp:397] relu1 -> conv1 (in-place)
I0130 16:30:48.850471 18755 net.cpp:150] Setting up relu1
I0130 16:30:48.850477 18755 net.cpp:157] Top shape: 1 96 300 500 (14400000)
I0130 16:30:48.850481 18755 net.cpp:165] Memory required for data: 136800108
I0130 16:30:48.850482 18755 layer_factory.hpp:77] Creating layer norm1
I0130 16:30:48.850489 18755 net.cpp:106] Creating Layer norm1
I0130 16:30:48.850492 18755 net.cpp:454] norm1 <- conv1
I0130 16:30:48.850495 18755 net.cpp:411] norm1 -> norm1
I0130 16:30:48.850576 18755 net.cpp:150] Setting up norm1
I0130 16:30:48.850582 18755 net.cpp:157] Top shape: 1 96 300 500 (14400000)
I0130 16:30:48.850584 18755 net.cpp:165] Memory required for data: 194400108
I0130 16:30:48.850586 18755 layer_factory.hpp:77] Creating layer pool1
I0130 16:30:48.850591 18755 net.cpp:106] Creating Layer pool1
I0130 16:30:48.850594 18755 net.cpp:454] pool1 <- norm1
I0130 16:30:48.850597 18755 net.cpp:411] pool1 -> pool1
I0130 16:30:48.850616 18755 net.cpp:150] Setting up pool1
I0130 16:30:48.850620 18755 net.cpp:157] Top shape: 1 96 151 251 (3638496)
I0130 16:30:48.850621 18755 net.cpp:165] Memory required for data: 208954092
I0130 16:30:48.850625 18755 layer_factory.hpp:77] Creating layer conv2
I0130 16:30:48.850632 18755 net.cpp:106] Creating Layer conv2
I0130 16:30:48.850634 18755 net.cpp:454] conv2 <- pool1
I0130 16:30:48.850637 18755 net.cpp:411] conv2 -> conv2
I0130 16:30:48.852658 18755 net.cpp:150] Setting up conv2
I0130 16:30:48.852669 18755 net.cpp:157] Top shape: 1 256 76 126 (2451456)
I0130 16:30:48.852671 18755 net.cpp:165] Memory required for data: 218759916
I0130 16:30:48.852679 18755 layer_factory.hpp:77] Creating layer relu2
I0130 16:30:48.852685 18755 net.cpp:106] Creating Layer relu2
I0130 16:30:48.852687 18755 net.cpp:454] relu2 <- conv2
I0130 16:30:48.852691 18755 net.cpp:397] relu2 -> conv2 (in-place)
I0130 16:30:48.852798 18755 net.cpp:150] Setting up relu2
I0130 16:30:48.852804 18755 net.cpp:157] Top shape: 1 256 76 126 (2451456)
I0130 16:30:48.852807 18755 net.cpp:165] Memory required for data: 228565740
I0130 16:30:48.852809 18755 layer_factory.hpp:77] Creating layer norm2
I0130 16:30:48.852815 18755 net.cpp:106] Creating Layer norm2
I0130 16:30:48.852818 18755 net.cpp:454] norm2 <- conv2
I0130 16:30:48.852824 18755 net.cpp:411] norm2 -> norm2
I0130 16:30:48.852893 18755 net.cpp:150] Setting up norm2
I0130 16:30:48.852898 18755 net.cpp:157] Top shape: 1 256 76 126 (2451456)
I0130 16:30:48.852901 18755 net.cpp:165] Memory required for data: 238371564
I0130 16:30:48.852903 18755 layer_factory.hpp:77] Creating layer pool2
I0130 16:30:48.852906 18755 net.cpp:106] Creating Layer pool2
I0130 16:30:48.852910 18755 net.cpp:454] pool2 <- norm2
I0130 16:30:48.852912 18755 net.cpp:411] pool2 -> pool2
I0130 16:30:48.852931 18755 net.cpp:150] Setting up pool2
I0130 16:30:48.852934 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.852936 18755 net.cpp:165] Memory required for data: 240927468
I0130 16:30:48.852938 18755 layer_factory.hpp:77] Creating layer conv3
I0130 16:30:48.852944 18755 net.cpp:106] Creating Layer conv3
I0130 16:30:48.852946 18755 net.cpp:454] conv3 <- pool2
I0130 16:30:48.852949 18755 net.cpp:411] conv3 -> conv3
I0130 16:30:48.856401 18755 net.cpp:150] Setting up conv3
I0130 16:30:48.856418 18755 net.cpp:157] Top shape: 1 384 39 64 (958464)
I0130 16:30:48.856421 18755 net.cpp:165] Memory required for data: 244761324
I0130 16:30:48.856431 18755 layer_factory.hpp:77] Creating layer relu3
I0130 16:30:48.856438 18755 net.cpp:106] Creating Layer relu3
I0130 16:30:48.856441 18755 net.cpp:454] relu3 <- conv3
I0130 16:30:48.856446 18755 net.cpp:397] relu3 -> conv3 (in-place)
I0130 16:30:48.856884 18755 net.cpp:150] Setting up relu3
I0130 16:30:48.856891 18755 net.cpp:157] Top shape: 1 384 39 64 (958464)
I0130 16:30:48.856894 18755 net.cpp:165] Memory required for data: 248595180
I0130 16:30:48.856896 18755 layer_factory.hpp:77] Creating layer conv4
I0130 16:30:48.856904 18755 net.cpp:106] Creating Layer conv4
I0130 16:30:48.856906 18755 net.cpp:454] conv4 <- conv3
I0130 16:30:48.856911 18755 net.cpp:411] conv4 -> conv4
I0130 16:30:48.859576 18755 net.cpp:150] Setting up conv4
I0130 16:30:48.859592 18755 net.cpp:157] Top shape: 1 384 39 64 (958464)
I0130 16:30:48.859594 18755 net.cpp:165] Memory required for data: 252429036
I0130 16:30:48.859601 18755 layer_factory.hpp:77] Creating layer relu4
I0130 16:30:48.859607 18755 net.cpp:106] Creating Layer relu4
I0130 16:30:48.859611 18755 net.cpp:454] relu4 <- conv4
I0130 16:30:48.859614 18755 net.cpp:397] relu4 -> conv4 (in-place)
I0130 16:30:48.859753 18755 net.cpp:150] Setting up relu4
I0130 16:30:48.859760 18755 net.cpp:157] Top shape: 1 384 39 64 (958464)
I0130 16:30:48.859761 18755 net.cpp:165] Memory required for data: 256262892
I0130 16:30:48.859763 18755 layer_factory.hpp:77] Creating layer conv5
I0130 16:30:48.859772 18755 net.cpp:106] Creating Layer conv5
I0130 16:30:48.859774 18755 net.cpp:454] conv5 <- conv4
I0130 16:30:48.859779 18755 net.cpp:411] conv5 -> conv5
I0130 16:30:48.862138 18755 net.cpp:150] Setting up conv5
I0130 16:30:48.862149 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.862152 18755 net.cpp:165] Memory required for data: 258818796
I0130 16:30:48.862160 18755 layer_factory.hpp:77] Creating layer relu5
I0130 16:30:48.862165 18755 net.cpp:106] Creating Layer relu5
I0130 16:30:48.862169 18755 net.cpp:454] relu5 <- conv5
I0130 16:30:48.862172 18755 net.cpp:397] relu5 -> conv5 (in-place)
I0130 16:30:48.862288 18755 net.cpp:150] Setting up relu5
I0130 16:30:48.862294 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.862296 18755 net.cpp:165] Memory required for data: 261374700
I0130 16:30:48.862298 18755 layer_factory.hpp:77] Creating layer conv5_relu5_0_split
I0130 16:30:48.862303 18755 net.cpp:106] Creating Layer conv5_relu5_0_split
I0130 16:30:48.862305 18755 net.cpp:454] conv5_relu5_0_split <- conv5
I0130 16:30:48.862308 18755 net.cpp:411] conv5_relu5_0_split -> conv5_relu5_0_split_0
I0130 16:30:48.862313 18755 net.cpp:411] conv5_relu5_0_split -> conv5_relu5_0_split_1
I0130 16:30:48.862339 18755 net.cpp:150] Setting up conv5_relu5_0_split
I0130 16:30:48.862342 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.862344 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.862346 18755 net.cpp:165] Memory required for data: 266486508
I0130 16:30:48.862349 18755 layer_factory.hpp:77] Creating layer rpn_conv/3x3
I0130 16:30:48.862355 18755 net.cpp:106] Creating Layer rpn_conv/3x3
I0130 16:30:48.862357 18755 net.cpp:454] rpn_conv/3x3 <- conv5_relu5_0_split_0
I0130 16:30:48.862361 18755 net.cpp:411] rpn_conv/3x3 -> rpn/output
I0130 16:30:48.877441 18755 net.cpp:150] Setting up rpn_conv/3x3
I0130 16:30:48.877470 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.877471 18755 net.cpp:165] Memory required for data: 269042412
I0130 16:30:48.877478 18755 layer_factory.hpp:77] Creating layer rpn_relu/3x3
I0130 16:30:48.877486 18755 net.cpp:106] Creating Layer rpn_relu/3x3
I0130 16:30:48.877490 18755 net.cpp:454] rpn_relu/3x3 <- rpn/output
I0130 16:30:48.877495 18755 net.cpp:397] rpn_relu/3x3 -> rpn/output (in-place)
I0130 16:30:48.877609 18755 net.cpp:150] Setting up rpn_relu/3x3
I0130 16:30:48.877614 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.877616 18755 net.cpp:165] Memory required for data: 271598316
I0130 16:30:48.877619 18755 layer_factory.hpp:77] Creating layer rpn/output_rpn_relu/3x3_0_split
I0130 16:30:48.877622 18755 net.cpp:106] Creating Layer rpn/output_rpn_relu/3x3_0_split
I0130 16:30:48.877625 18755 net.cpp:454] rpn/output_rpn_relu/3x3_0_split <- rpn/output
I0130 16:30:48.877629 18755 net.cpp:411] rpn/output_rpn_relu/3x3_0_split -> rpn/output_rpn_relu/3x3_0_split_0
I0130 16:30:48.877634 18755 net.cpp:411] rpn/output_rpn_relu/3x3_0_split -> rpn/output_rpn_relu/3x3_0_split_1
I0130 16:30:48.877658 18755 net.cpp:150] Setting up rpn/output_rpn_relu/3x3_0_split
I0130 16:30:48.877661 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.877665 18755 net.cpp:157] Top shape: 1 256 39 64 (638976)
I0130 16:30:48.877666 18755 net.cpp:165] Memory required for data: 276710124
I0130 16:30:48.877668 18755 layer_factory.hpp:77] Creating layer rpn_cls_score
I0130 16:30:48.877676 18755 net.cpp:106] Creating Layer rpn_cls_score
I0130 16:30:48.877678 18755 net.cpp:454] rpn_cls_score <- rpn/output_rpn_relu/3x3_0_split_0
I0130 16:30:48.877682 18755 net.cpp:411] rpn_cls_score -> rpn_cls_score
I0130 16:30:48.878798 18755 net.cpp:150] Setting up rpn_cls_score
I0130 16:30:48.878810 18755 net.cpp:157] Top shape: 1 36 39 64 (89856)
I0130 16:30:48.878813 18755 net.cpp:165] Memory required for data: 277069548
I0130 16:30:48.878818 18755 layer_factory.hpp:77] Creating layer rpn_cls_score_rpn_cls_score_0_split
I0130 16:30:48.878826 18755 net.cpp:106] Creating Layer rpn_cls_score_rpn_cls_score_0_split
I0130 16:30:48.878829 18755 net.cpp:454] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score
I0130 16:30:48.878834 18755 net.cpp:411] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0
I0130 16:30:48.878839 18755 net.cpp:411] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1
I0130 16:30:48.878865 18755 net.cpp:150] Setting up rpn_cls_score_rpn_cls_score_0_split
I0130 16:30:48.878867 18755 net.cpp:157] Top shape: 1 36 39 64 (89856)
I0130 16:30:48.878870 18755 net.cpp:157] Top shape: 1 36 39 64 (89856)
I0130 16:30:48.878872 18755 net.cpp:165] Memory required for data: 277788396
I0130 16:30:48.878875 18755 layer_factory.hpp:77] Creating layer rpn_bbox_pred
I0130 16:30:48.878883 18755 net.cpp:106] Creating Layer rpn_bbox_pred
I0130 16:30:48.878885 18755 net.cpp:454] rpn_bbox_pred <- rpn/output_rpn_relu/3x3_0_split_1
I0130 16:30:48.878890 18755 net.cpp:411] rpn_bbox_pred -> rpn_bbox_pred
I0130 16:30:48.882532 18755 net.cpp:150] Setting up rpn_bbox_pred
I0130 16:30:48.882593 18755 net.cpp:157] Top shape: 1 90 39 64 (224640)
I0130 16:30:48.882598 18755 net.cpp:165] Memory required for data: 278686956
I0130 16:30:48.882612 18755 layer_factory.hpp:77] Creating layer rpn_bbox_pred_rpn_bbox_pred_0_split
I0130 16:30:48.882623 18755 net.cpp:106] Creating Layer rpn_bbox_pred_rpn_bbox_pred_0_split
I0130 16:30:48.882630 18755 net.cpp:454] rpn_bbox_pred_rpn_bbox_pred_0_split <- rpn_bbox_pred
I0130 16:30:48.882639 18755 net.cpp:411] rpn_bbox_pred_rpn_bbox_pred_0_split -> rpn_bbox_pred_rpn_bbox_pred_0_split_0
I0130 16:30:48.882649 18755 net.cpp:411] rpn_bbox_pred_rpn_bbox_pred_0_split -> rpn_bbox_pred_rpn_bbox_pred_0_split_1
I0130 16:30:48.882685 18755 net.cpp:150] Setting up rpn_bbox_pred_rpn_bbox_pred_0_split
I0130 16:30:48.882690 18755 net.cpp:157] Top shape: 1 90 39 64 (224640)
I0130 16:30:48.882694 18755 net.cpp:157] Top shape: 1 90 39 64 (224640)
I0130 16:30:48.882695 18755 net.cpp:165] Memory required for data: 280484076
I0130 16:30:48.882697 18755 layer_factory.hpp:77] Creating layer rpn_cls_score_reshape
I0130 16:30:48.882704 18755 net.cpp:106] Creating Layer rpn_cls_score_reshape
I0130 16:30:48.882706 18755 net.cpp:454] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0
I0130 16:30:48.882711 18755 net.cpp:411] rpn_cls_score_reshape -> rpn_cls_score_reshape
I0130 16:30:48.882727 18755 net.cpp:150] Setting up rpn_cls_score_reshape
I0130 16:30:48.882731 18755 net.cpp:157] Top shape: 1 2 702 64 (89856)
I0130 16:30:48.882733 18755 net.cpp:165] Memory required for data: 280843500
I0130 16:30:48.882735 18755 layer_factory.hpp:77] Creating layer rpn_cls_score_reshape_rpn_cls_score_reshape_0_split
I0130 16:30:48.882738 18755 net.cpp:106] Creating Layer rpn_cls_score_reshape_rpn_cls_score_reshape_0_split
I0130 16:30:48.882741 18755 net.cpp:454] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split <- rpn_cls_score_reshape
I0130 16:30:48.882745 18755 net.cpp:411] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split -> rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_0
I0130 16:30:48.882748 18755 net.cpp:411] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split -> rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_1
I0130 16:30:48.882771 18755 net.cpp:150] Setting up rpn_cls_score_reshape_rpn_cls_score_reshape_0_split
I0130 16:30:48.882774 18755 net.cpp:157] Top shape: 1 2 702 64 (89856)
I0130 16:30:48.882777 18755 net.cpp:157] Top shape: 1 2 702 64 (89856)
I0130 16:30:48.882779 18755 net.cpp:165] Memory required for data: 281562348
I0130 16:30:48.882781 18755 layer_factory.hpp:77] Creating layer rpn-data
I0130 16:30:48.883280 18755 net.cpp:106] Creating Layer rpn-data
I0130 16:30:48.883287 18755 net.cpp:454] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1
I0130 16:30:48.883291 18755 net.cpp:454] rpn-data <- gt_boxes_input-data_2_split_0
I0130 16:30:48.883294 18755 net.cpp:454] rpn-data <- im_info_input-data_1_split_0
I0130 16:30:48.883298 18755 net.cpp:454] rpn-data <- data_input-data_0_split_1
I0130 16:30:48.883301 18755 net.cpp:411] rpn-data -> rpn_labels
I0130 16:30:48.883316 18755 net.cpp:411] rpn-data -> rpn_bbox_targets
I0130 16:30:48.883321 18755 net.cpp:411] rpn-data -> rpn_bbox_inside_weights
I0130 16:30:48.883324 18755 net.cpp:411] rpn-data -> rpn_bbox_outside_weights
I0130 16:30:48.884225 18755 net.cpp:150] Setting up rpn-data
I0130 16:30:48.884250 18755 net.cpp:157] Top shape: 1 1 702 64 (44928)
I0130 16:30:48.884256 18755 net.cpp:157] Top shape: 1 90 39 64 (224640)
I0130 16:30:48.884261 18755 net.cpp:157] Top shape: 1 90 39 64 (224640)
I0130 16:30:48.884265 18755 net.cpp:157] Top shape: 1 90 39 64 (224640)
I0130 16:30:48.884268 18755 net.cpp:165] Memory required for data: 284437740
I0130 16:30:48.884270 18755 layer_factory.hpp:77] Creating layer rpn_loss_cls
I0130 16:30:48.884276 18755 net.cpp:106] Creating Layer rpn_loss_cls
I0130 16:30:48.884279 18755 net.cpp:454] rpn_loss_cls <- rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_0
I0130 16:30:48.884284 18755 net.cpp:454] rpn_loss_cls <- rpn_labels
I0130 16:30:48.884289 18755 net.cpp:411] rpn_loss_cls -> rpn_cls_loss
I0130 16:30:48.884294 18755 layer_factory.hpp:77] Creating layer rpn_loss_cls
I0130 16:30:48.884610 18755 net.cpp:150] Setting up rpn_loss_cls
I0130 16:30:48.884618 18755 net.cpp:157] Top shape: (1)
I0130 16:30:48.884619 18755 net.cpp:160] with loss weight 1
I0130 16:30:48.884629 18755 net.cpp:165] Memory required for data: 284437744
I0130 16:30:48.884631 18755 layer_factory.hpp:77] Creating layer rpn_loss_bbox
I0130 16:30:48.884637 18755 net.cpp:106] Creating Layer rpn_loss_bbox
I0130 16:30:48.884649 18755 net.cpp:454] rpn_loss_bbox <- rpn_bbox_pred_rpn_bbox_pred_0_split_0
I0130 16:30:48.884654 18755 net.cpp:454] rpn_loss_bbox <- rpn_bbox_targets
I0130 16:30:48.884656 18755 net.cpp:454] rpn_loss_bbox <- rpn_bbox_inside_weights
I0130 16:30:48.884668 18755 net.cpp:454] rpn_loss_bbox <- rpn_bbox_outside_weights
I0130 16:30:48.884672 18755 net.cpp:411] rpn_loss_bbox -> rpn_loss_bbox
I0130 16:30:48.887003 18755 net.cpp:150] Setting up rpn_loss_bbox
I0130 16:30:48.887028 18755 net.cpp:157] Top shape: (1)
I0130 16:30:48.887032 18755 net.cpp:160] with loss weight 1
I0130 16:30:48.887040 18755 net.cpp:165] Memory required for data: 284437748
I0130 16:30:48.887045 18755 layer_factory.hpp:77] Creating layer rpn_cls_prob
I0130 16:30:48.887055 18755 net.cpp:106] Creating Layer rpn_cls_prob
I0130 16:30:48.887060 18755 net.cpp:454] rpn_cls_prob <- rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_1
I0130 16:30:48.887068 18755 net.cpp:411] rpn_cls_prob -> rpn_cls_prob
I0130 16:30:48.887296 18755 net.cpp:150] Setting up rpn_cls_prob
I0130 16:30:48.887303 18755 net.cpp:157] Top shape: 1 2 702 64 (89856)
I0130 16:30:48.887306 18755 net.cpp:165] Memory required for data: 284797172
I0130 16:30:48.887308 18755 layer_factory.hpp:77] Creating layer rpn_cls_prob_reshape
I0130 16:30:48.887320 18755 net.cpp:106] Creating Layer rpn_cls_prob_reshape
I0130 16:30:48.887323 18755 net.cpp:454] rpn_cls_prob_reshape <- rpn_cls_prob
I0130 16:30:48.887327 18755 net.cpp:411] rpn_cls_prob_reshape -> rpn_cls_prob_reshape
I0130 16:30:48.887348 18755 net.cpp:150] Setting up rpn_cls_prob_reshape
I0130 16:30:48.887352 18755 net.cpp:157] Top shape: 1 36 39 64 (89856)
I0130 16:30:48.887354 18755 net.cpp:165] Memory required for data: 285156596
I0130 16:30:48.887356 18755 layer_factory.hpp:77] Creating layer proposal
I0130 16:30:48.887778 18755 net.cpp:106] Creating Layer proposal
I0130 16:30:48.887786 18755 net.cpp:454] proposal <- rpn_cls_prob_reshape
I0130 16:30:48.887790 18755 net.cpp:454] proposal <- rpn_bbox_pred_rpn_bbox_pred_0_split_1
I0130 16:30:48.887794 18755 net.cpp:454] proposal <- im_info_input-data_1_split_1
I0130 16:30:48.887797 18755 net.cpp:411] proposal -> rpn_rois
I0130 16:30:48.889271 18755 net.cpp:150] Setting up proposal
I0130 16:30:48.889283 18755 net.cpp:157] Top shape: 1 6 (6)
I0130 16:30:48.889286 18755 net.cpp:165] Memory required for data: 285156620
I0130 16:30:48.889289 18755 layer_factory.hpp:77] Creating layer roi-data
I0130 16:30:48.889443 18755 net.cpp:106] Creating Layer roi-data
I0130 16:30:48.889451 18755 net.cpp:454] roi-data <- rpn_rois
I0130 16:30:48.889456 18755 net.cpp:454] roi-data <- gt_boxes_input-data_2_split_1
I0130 16:30:48.889459 18755 net.cpp:411] roi-data -> rois
I0130 16:30:48.889466 18755 net.cpp:411] roi-data -> labels
I0130 16:30:48.889470 18755 net.cpp:411] roi-data -> bbox_targets
I0130 16:30:48.889474 18755 net.cpp:411] roi-data -> bbox_inside_weights
I0130 16:30:48.889478 18755 net.cpp:411] roi-data -> bbox_outside_weights
I0130 16:30:48.889778 18755 net.cpp:150] Setting up roi-data
I0130 16:30:48.889786 18755 net.cpp:157] Top shape: 1 6 (6)
I0130 16:30:48.889789 18755 net.cpp:157] Top shape: 1 1 (1)
I0130 16:30:48.889791 18755 net.cpp:157] Top shape: 1 10 (10)
I0130 16:30:48.889794 18755 net.cpp:157] Top shape: 1 10 (10)
I0130 16:30:48.889797 18755 net.cpp:157] Top shape: 1 10 (10)
I0130 16:30:48.889799 18755 net.cpp:165] Memory required for data: 285156768
I0130 16:30:48.889801 18755 layer_factory.hpp:77] Creating layer roi_pool_conv5
I0130 16:30:48.889807 18755 net.cpp:106] Creating Layer roi_pool_conv5
I0130 16:30:48.889811 18755 net.cpp:454] roi_pool_conv5 <- conv5_relu5_0_split_1
I0130 16:30:48.889814 18755 net.cpp:454] roi_pool_conv5 <- rois
I0130 16:30:48.889817 18755 net.cpp:454] roi_pool_conv5 <- im_info_input-data_1_split_2
I0130 16:30:48.889822 18755 net.cpp:411] roi_pool_conv5 -> roi_pool_conv5
I0130 16:30:48.889827 18755 rotate_roi_pooling_layer.cpp:24] Spatial scale: 0.0625
I0130 16:30:48.889858 18755 net.cpp:150] Setting up roi_pool_conv5
I0130 16:30:48.889861 18755 net.cpp:157] Top shape: 1 256 6 6 (9216)
I0130 16:30:48.889864 18755 net.cpp:165] Memory required for data: 285193632
I0130 16:30:48.889866 18755 layer_factory.hpp:77] Creating layer fc6
I0130 16:30:48.889871 18755 net.cpp:106] Creating Layer fc6
I0130 16:30:48.889874 18755 net.cpp:454] fc6 <- roi_pool_conv5
I0130 16:30:48.889878 18755 net.cpp:411] fc6 -> fc6
3.ProposalTargetLayer setup 第三次产生Anchors完成
3.ProposalTargetLayer 调用了reshape 第三次产生Anchors
I0130 16:30:48.976393 18755 net.cpp:150] Setting up fc6
I0130 16:30:48.976436 18755 net.cpp:157] Top shape: 1 4096 (4096)
I0130 16:30:48.976444 18755 net.cpp:165] Memory required for data: 285210016
I0130 16:30:48.976469 18755 layer_factory.hpp:77] Creating layer relu6
I0130 16:30:48.976486 18755 net.cpp:106] Creating Layer relu6
I0130 16:30:48.976495 18755 net.cpp:454] relu6 <- fc6
I0130 16:30:48.976505 18755 net.cpp:397] relu6 -> fc6 (in-place)
I0130 16:30:48.977505 18755 net.cpp:150] Setting up relu6
I0130 16:30:48.977529 18755 net.cpp:157] Top shape: 1 4096 (4096)
I0130 16:30:48.977533 18755 net.cpp:165] Memory required for data: 285226400
I0130 16:30:48.977540 18755 layer_factory.hpp:77] Creating layer drop6
I0130 16:30:48.977551 18755 net.cpp:106] Creating Layer drop6
I0130 16:30:48.977558 18755 net.cpp:454] drop6 <- fc6
I0130 16:30:48.977566 18755 net.cpp:397] drop6 -> fc6 (in-place)
I0130 16:30:48.977615 18755 net.cpp:150] Setting up drop6
I0130 16:30:48.977622 18755 net.cpp:157] Top shape: 1 4096 (4096)
I0130 16:30:48.977624 18755 net.cpp:165] Memory required for data: 285242784
I0130 16:30:48.977628 18755 layer_factory.hpp:77] Creating layer fc7
I0130 16:30:48.977635 18755 net.cpp:106] Creating Layer fc7
I0130 16:30:48.977638 18755 net.cpp:454] fc7 <- fc6
I0130 16:30:48.977643 18755 net.cpp:411] fc7 -> fc7
I0130 16:30:49.007350 18755 net.cpp:150] Setting up fc7
I0130 16:30:49.007377 18755 net.cpp:157] Top shape: 1 4096 (4096)
I0130 16:30:49.007381 18755 net.cpp:165] Memory required for data: 285259168
I0130 16:30:49.007390 18755 layer_factory.hpp:77] Creating layer relu7
I0130 16:30:49.007400 18755 net.cpp:106] Creating Layer relu7
I0130 16:30:49.007403 18755 net.cpp:454] relu7 <- fc7
I0130 16:30:49.007410 18755 net.cpp:397] relu7 -> fc7 (in-place)
I0130 16:30:49.007612 18755 net.cpp:150] Setting up relu7
I0130 16:30:49.007622 18755 net.cpp:157] Top shape: 1 4096 (4096)
I0130 16:30:49.007623 18755 net.cpp:165] Memory required for data: 285275552
I0130 16:30:49.007627 18755 layer_factory.hpp:77] Creating layer drop7
I0130 16:30:49.007633 18755 net.cpp:106] Creating Layer drop7
I0130 16:30:49.007637 18755 net.cpp:454] drop7 <- fc7
I0130 16:30:49.007640 18755 net.cpp:397] drop7 -> fc7 (in-place)
I0130 16:30:49.007668 18755 net.cpp:150] Setting up drop7
I0130 16:30:49.007673 18755 net.cpp:157] Top shape: 1 4096 (4096)
I0130 16:30:49.007674 18755 net.cpp:165] Memory required for data: 285291936
I0130 16:30:49.007678 18755 layer_factory.hpp:77] Creating layer fc7_drop7_0_split
I0130 16:30:49.007681 18755 net.cpp:106] Creating Layer fc7_drop7_0_split
I0130 16:30:49.007684 18755 net.cpp:454] fc7_drop7_0_split <- fc7
I0130 16:30:49.007688 18755 net.cpp:411] fc7_drop7_0_split -> fc7_drop7_0_split_0
I0130 16:30:49.007692 18755 net.cpp:411] fc7_drop7_0_split -> fc7_drop7_0_split_1
I0130 16:30:49.007725 18755 net.cpp:150] Setting up fc7_drop7_0_split
I0130 16:30:49.007730 18755 net.cpp:157] Top shape: 1 4096 (4096)
I0130 16:30:49.007731 18755 net.cpp:157] Top shape: 1 4096 (4096)
I0130 16:30:49.007733 18755 net.cpp:165] Memory required for data: 285324704
I0130 16:30:49.007736 18755 layer_factory.hpp:77] Creating layer cls_score
I0130 16:30:49.007741 18755 net.cpp:106] Creating Layer cls_score
I0130 16:30:49.007745 18755 net.cpp:454] cls_score <- fc7_drop7_0_split_0
I0130 16:30:49.007747 18755 net.cpp:411] cls_score -> cls_score
I0130 16:30:49.007972 18755 net.cpp:150] Setting up cls_score
I0130 16:30:49.007977 18755 net.cpp:157] Top shape: 1 2 (2)
I0130 16:30:49.007979 18755 net.cpp:165] Memory required for data: 285324712
I0130 16:30:49.007984 18755 layer_factory.hpp:77] Creating layer bbox_pred
I0130 16:30:49.007989 18755 net.cpp:106] Creating Layer bbox_pred
I0130 16:30:49.007992 18755 net.cpp:454] bbox_pred <- fc7_drop7_0_split_1
I0130 16:30:49.007997 18755 net.cpp:411] bbox_pred -> bbox_pred
I0130 16:30:49.009044 18755 net.cpp:150] Setting up bbox_pred
I0130 16:30:49.009057 18755 net.cpp:157] Top shape: 1 10 (10)
I0130 16:30:49.009058 18755 net.cpp:165] Memory required for data: 285324752
I0130 16:30:49.009063 18755 layer_factory.hpp:77] Creating layer loss_cls
I0130 16:30:49.009070 18755 net.cpp:106] Creating Layer loss_cls
I0130 16:30:49.009073 18755 net.cpp:454] loss_cls <- cls_score
I0130 16:30:49.009078 18755 net.cpp:454] loss_cls <- labels
I0130 16:30:49.009083 18755 net.cpp:411] loss_cls -> cls_loss
I0130 16:30:49.009089 18755 layer_factory.hpp:77] Creating layer loss_cls
I0130 16:30:49.009377 18755 net.cpp:150] Setting up loss_cls
I0130 16:30:49.009385 18755 net.cpp:157] Top shape: (1)
I0130 16:30:49.009388 18755 net.cpp:160] with loss weight 1
I0130 16:30:49.009399 18755 net.cpp:165] Memory required for data: 285324756
I0130 16:30:49.009402 18755 layer_factory.hpp:77] Creating layer loss_bbox
I0130 16:30:49.009408 18755 net.cpp:106] Creating Layer loss_bbox
I0130 16:30:49.009413 18755 net.cpp:454] loss_bbox <- bbox_pred
I0130 16:30:49.009415 18755 net.cpp:454] loss_bbox <- bbox_targets
I0130 16:30:49.009418 18755 net.cpp:454] loss_bbox <- bbox_inside_weights
I0130 16:30:49.009421 18755 net.cpp:454] loss_bbox <- bbox_outside_weights
I0130 16:30:49.009425 18755 net.cpp:411] loss_bbox -> bbox_loss
I0130 16:30:49.009531 18755 net.cpp:150] Setting up loss_bbox
I0130 16:30:49.009536 18755 net.cpp:157] Top shape: (1)
I0130 16:30:49.009538 18755 net.cpp:160] with loss weight 1
I0130 16:30:49.009543 18755 net.cpp:165] Memory required for data: 285324760
I0130 16:30:49.009547 18755 net.cpp:226] loss_bbox needs backward computation.
I0130 16:30:49.009551 18755 net.cpp:226] loss_cls needs backward computation.
I0130 16:30:49.009553 18755 net.cpp:226] bbox_pred needs backward computation.
I0130 16:30:49.009557 18755 net.cpp:226] cls_score needs backward computation.
I0130 16:30:49.009559 18755 net.cpp:226] fc7_drop7_0_split needs backward computation.
I0130 16:30:49.009562 18755 net.cpp:226] drop7 needs backward computation.
I0130 16:30:49.009564 18755 net.cpp:226] relu7 needs backward computation.
I0130 16:30:49.009567 18755 net.cpp:226] fc7 needs backward computation.
I0130 16:30:49.009569 18755 net.cpp:226] drop6 needs backward computation.
I0130 16:30:49.009572 18755 net.cpp:226] relu6 needs backward computation.
I0130 16:30:49.009574 18755 net.cpp:226] fc6 needs backward computation.
I0130 16:30:49.009577 18755 net.cpp:226] roi_pool_conv5 needs backward computation.
I0130 16:30:49.009582 18755 net.cpp:226] roi-data needs backward computation.
I0130 16:30:49.009585 18755 net.cpp:226] proposal needs backward computation.
I0130 16:30:49.009589 18755 net.cpp:226] rpn_cls_prob_reshape needs backward computation.
I0130 16:30:49.009593 18755 net.cpp:226] rpn_cls_prob needs backward computation.
I0130 16:30:49.009595 18755 net.cpp:226] rpn_loss_bbox needs backward computation.
I0130 16:30:49.009599 18755 net.cpp:226] rpn_loss_cls needs backward computation.
I0130 16:30:49.009603 18755 net.cpp:226] rpn-data needs backward computation.
I0130 16:30:49.009608 18755 net.cpp:226] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split needs backward computation.
I0130 16:30:49.009611 18755 net.cpp:226] rpn_cls_score_reshape needs backward computation.
I0130 16:30:49.009614 18755 net.cpp:226] rpn_bbox_pred_rpn_bbox_pred_0_split needs backward computation.
I0130 16:30:49.009618 18755 net.cpp:226] rpn_bbox_pred needs backward computation.
I0130 16:30:49.009621 18755 net.cpp:226] rpn_cls_score_rpn_cls_score_0_split needs backward computation.
I0130 16:30:49.009624 18755 net.cpp:226] rpn_cls_score needs backward computation.
I0130 16:30:49.009627 18755 net.cpp:226] rpn/output_rpn_relu/3x3_0_split needs backward computation.
I0130 16:30:49.009631 18755 net.cpp:226] rpn_relu/3x3 needs backward computation.
I0130 16:30:49.009634 18755 net.cpp:226] rpn_conv/3x3 needs backward computation.
I0130 16:30:49.009636 18755 net.cpp:226] conv5_relu5_0_split needs backward computation.
I0130 16:30:49.009640 18755 net.cpp:226] relu5 needs backward computation.
I0130 16:30:49.009644 18755 net.cpp:226] conv5 needs backward computation.
I0130 16:30:49.009645 18755 net.cpp:226] relu4 needs backward computation.
I0130 16:30:49.009649 18755 net.cpp:226] conv4 needs backward computation.
I0130 16:30:49.009651 18755 net.cpp:226] relu3 needs backward computation.
I0130 16:30:49.009654 18755 net.cpp:226] conv3 needs backward computation.
I0130 16:30:49.009657 18755 net.cpp:226] pool2 needs backward computation.
I0130 16:30:49.009660 18755 net.cpp:226] norm2 needs backward computation.
I0130 16:30:49.009663 18755 net.cpp:226] relu2 needs backward computation.
I0130 16:30:49.009665 18755 net.cpp:226] conv2 needs backward computation.
I0130 16:30:49.009668 18755 net.cpp:226] pool1 needs backward computation.
I0130 16:30:49.009671 18755 net.cpp:226] norm1 needs backward computation.
I0130 16:30:49.009673 18755 net.cpp:226] relu1 needs backward computation.
I0130 16:30:49.009676 18755 net.cpp:226] conv1 needs backward computation.
I0130 16:30:49.009680 18755 net.cpp:228] gt_boxes_input-data_2_split does not need backward computation.
I0130 16:30:49.009683 18755 net.cpp:228] im_info_input-data_1_split does not need backward computation.
I0130 16:30:49.009686 18755 net.cpp:228] data_input-data_0_split does not need backward computation.
I0130 16:30:49.009690 18755 net.cpp:228] input-data does not need backward computation.
I0130 16:30:49.009692 18755 net.cpp:270] This network produces output bbox_loss
I0130 16:30:49.009694 18755 net.cpp:270] This network produces output cls_loss
I0130 16:30:49.009697 18755 net.cpp:270] This network produces output rpn_cls_loss
I0130 16:30:49.009701 18755 net.cpp:270] This network produces output rpn_loss_bbox
I0130 16:30:49.009732 18755 net.cpp:283] Network initialization done.
I0130 16:30:49.009883 18755 solver.cpp:60] Solver scaffolding done.
Loading pretrained model weights from /home/x306/wzy/py-faster-rcnn/data/imagenet_models/ZF.v2.caffemodel
I0130 16:30:49.328835 18755 net.cpp:816] Ignoring source layer pool5_spm6
I0130 16:30:49.328855 18755 net.cpp:816] Ignoring source layer pool5_spm6_flatten
I0130 16:30:49.385639 18755 net.cpp:816] Ignoring source layer fc8
I0130 16:30:49.385661 18755 net.cpp:816] Ignoring source layer prob
I0130 16:30:50.523666 18755 solver.cpp:229] Iteration 0, loss = 1.41919
I0130 16:30:50.523718 18755 solver.cpp:245] Train net output #0: bbox_loss = 0.000346801 (* 1 = 0.000346801 loss)
I0130 16:30:50.523727 18755 solver.cpp:245] Train net output #1: cls_loss = 0.539508 (* 1 = 0.539508 loss)
I0130 16:30:50.523735 18755 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.75761 (* 1 = 0.75761 loss)
I0130 16:30:50.523741 18755 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0 (* 1 = 0 loss)
I0130 16:30:50.523749 18755 sgd_solver.cpp:106] Iteration 0, lr = 0.001
F0130 16:30:50.527855 18755 sgd_solver.cu:19] Check failed: error == cudaSuccess (11 vs. 0) invalid argument
*** Check failure stack trace: ***
Process finished with exit code 134 (interrupted by signal 6: SIGABRT)
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mjq11302010044
commented on September 16, 2024
@wzhiyuan2016 Sorry, but I notice that you are using ZF-net to train RRPN. But I think VGG16 can work well.
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Related Issues (20)
- Can I test the rotation_demo.py with CPU and if yes what changes need to be done with set up file?
- ImportError: No module named _caffe HOT 1
- orders of rotate box in nms and iou HOT 2
- Implementation Details of rotate_roi_pooling_layer.cpp HOT 2
- 使用MSRA-TD500训练，测试时没有结果 HOT 6
- Rotated IOU greater than 1 HOT 2
- Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) HOT 1
- 版本咨询
- How to deal with true angle and predict angle has 180° error? HOT 2
- can not train model
- while proposals is [], namely null list, rot_polygon_nms.cpp will raise error.
- ValueError: could not broadcast input array from shape (3,5) into shape (3,4)
- illegal memory access was encountered
- cudnn_conv_layer.cpp:53] Check failed: status == CUDNN_STATUS_SUCCESS (4 vs. 0) CUDNN_STATUS_INTERNAL_ERROR HOT 1
- 大神你好，怎么移植rrpn的架构进入mask R-cnn中 HOT 1
- 有没有更加详细的训练自己模型的介绍，新手有些看不明白
- 复现论文精度
- vis_image
- GT框的倾斜角θ的输入 HOT 1
- rotate_roi_align_layer.cu(147): error
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