mitosis detection
问题一
root@b65afb23bc00:/home/lyh/mitosis_DET/DeepMitosis/DeepDetection# ./experiments/scripts/faster_rcnn_end2end_12.sh 0 VGG_CNN_M_1024_Scale mitos --set EXP_DIR XX
+ set -e
+ export PYTHONUNBUFFERED=True
+ PYTHONUNBUFFERED=True
+ GPU_ID=0
+ NET=VGG_CNN_M_1024_Scale
+ NET_lc=vgg_cnn_m_1024_scale
+ DATASET=mitos
+ array=($@)
+ len=6
+ EXTRA_ARGS='--set EXP_DIR XX'
+ EXTRA_ARGS_SLUG=--set_EXP_DIR_XX
+ case $DATASET in
+ TRAIN_IMDB=mitos_2012_train
+ TEST_IMDB=mitos_2012_test
+ PT_DIR=mitos
+ ITERS=150000
++ date +%Y-%m-%d_%H-%M-%S
+ LOG=experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-02-29_09-17-53
+ exec
++ tee -a experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-02-29_09-17-53
+ echo Logging output to experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-02-29_09-17-53
Logging output to experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-02-29_09-17-53
+ ./tools/train_net.py --gpu 0 --solver models/mitos/VGG_CNN_M_1024_Scale/faster_rcnn_end2end/solver.prototxt --weights data/imagenet_models/VGG_CNN_M_1024_Scale.v2.caffemodel --imdb mitos_2012_train --iters 150000 --cfg experiments/cfgs/faster_rcnn_end2end_2012.yml --set EXP_DIR XX
Called with args:
Namespace(cfg_file='experiments/cfgs/faster_rcnn_end2end_2012.yml', gpu_id=0, imdb_name='mitos_2012_train', max_iters=150000, pretrained_model='data/imagenet_models/VGG_CNN_M_1024_Scale.v2.caffemodel', randomize=False, set_cfgs=['EXP_DIR', 'XX'], solver='models/mitos/VGG_CNN_M_1024_Scale/faster_rcnn_end2end/solver.prototxt')
Using config:
{'DATA_DIR': '/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/data','DEDUP_BOXES': 0.0625,'EPS': 1e-14,'EXP_DIR': 'XX','GPU_ID': 0,'MATLAB': 'matlab','MODELS_DIR': '/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/models/pascal_voc','PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),'RNG_SEED': 3,'ROOT_DIR': '/home/lyh/mitosis_DET/DeepMitosis/DeepDetection','TEST': {'BBOX_REG': True,'HAS_RPN': True,'MAX_SIZE': 4168,'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': [4168],'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': 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,'HAS_RPN': True,'IMS_PER_BATCH': 1,'MAX_SIZE': 1024,'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': [1024],'SNAPSHOT_INFIX': '','SNAPSHOT_ITERS': 10000,'USE_FLIPPED': True,'USE_PREFETCH': False},'USE_GPU_NMS': True}Traceback (most recent call last):File "./tools/train_net.py", line 104, in <module>imdb, roidb = combined_roidb(args.imdb_name)File "./tools/train_net.py", line 69, in combined_roidbroidbs = [get_roidb(s) for s in imdb_names.split('+')]File "./tools/train_net.py", line 62, in get_roidbimdb = get_imdb(imdb_name)File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/datasets/factory.py", line 51, in get_imdbreturn __sets[name]()File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/datasets/factory.py", line 40, in <lambda>__sets[name] = (lambda split=split, year=year: mitos(split, year))File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/datasets/mitos.py", line 34, in __init__self._image_index = self._load_image_set_index()File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/datasets/mitos.py", line 80, in _load_image_set_index'Path does not exist: {}'.format(image_set_file)
AssertionError: Path does not exist: /home/lyh/mitosis_DET/DeepMitosis/DeepDetection/data/Mitos2012/ImageSets/train.txt问题二：
root@b65afb23bc00:/home/lyh/mitosis_DET/DeepMitosis/DeepDetection# ./experiments/scripts/faster_rcnn_end2end_test_12.sh 6 VGG_CNN_M_1024_Scale mitos --set EXP_DIR XX
+ set -e
+ export PYTHONUNBUFFERED=True
+ PYTHONUNBUFFERED=True
+ GPU_ID=6
+ NET=VGG_CNN_M_1024_Scale
+ NET_lc=vgg_cnn_m_1024_scale
+ DATASET=mitos
+ array=($@)
+ len=6
+ EXTRA_ARGS='--set EXP_DIR XX'
+ EXTRA_ARGS_SLUG=--set_EXP_DIR_XX
+ case $DATASET in
+ TEST_IMDB=mitos_2012_test
+ PT_DIR=mitos
+ ITERS=150000
++ date +%Y-%m-%d_%H-%M-%S
+ LOG=experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-02-29_09-26-19
+ exec
++ tee -a experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-02-29_09-26-19
+ echo Logging output to experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-02-29_09-26-19
Logging output to experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-02-29_09-26-19
+ NET_FINAL=output/final_git/detection_12.caffemodel
+ ./tools/test_net.py --gpu 6 --def models/mitos/VGG_CNN_M_1024_Scale/faster_rcnn_end2end/test.prototxt --net output/final_git/detection_12.caffemodel --imdb mitos_2012_test --cfg experiments/cfgs/faster_rcnn_end2end_2012.yml --set EXP_DIR XX
Traceback (most recent call last):File "./tools/test_net.py", line 13, in <module>from fast_rcnn.test import test_netFile "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/fast_rcnn/test.py", line 17, in <module>from fast_rcnn.nms_wrapper import nmsFile "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/fast_rcnn/nms_wrapper.py", line 9, in <module>from nms.gpu_nms import gpu_nms
ImportError: libcudart.so.7.5: cannot open shared object file: No such file or directory解决方法：参考https://blog.csdn.net/qq_34597886/article/details/78562343
这个链接的方法对我没用，也放在这https://blog.csdn.net/farphone/article/details/86744413?depth_1-utm_source=distribute.pc_relevant.none-task&utm_source=distribute.pc_relevant.none-task
cd /usr/local/
root@b65afb23bc00:/usr/local# ls
bin cuda cuda-8.0 etc games include lib man sbin share src
root@b65afb23bc00:/usr/local# cd cuda
root@b65afb23bc00:/usr/local/cuda# ls
LICENSE bin extras lib64 nvvm src tools
README doc include nvml share targets version.txt
root@b65afb23bc00:/usr/local/cuda# cd lib64
root@b65afb23bc00:/usr/local/cuda/lib64# ls
libOpenCL.so libcuinj64.so.8.0.61 libnppicc.so libnppitc.so.8.0
libOpenCL.so.1 libculibos.a libnppicc.so.8.0 libnppitc.so.8.0.61
libOpenCL.so.1.0 libcurand.so libnppicc.so.8.0.61 libnpps.so
libOpenCL.so.1.0.0 libcurand.so.8.0 libnppicom.so libnpps.so.8.0
libcublas.so libcurand.so.8.0.61 libnppicom.so.8.0 libnpps.so.8.0.61
libcublas.so.8.0 libcurand_static.a libnppicom.so.8.0.61 libnpps_static.a
libcublas.so.8.0.61 libcusolver.so libnppidei.so libnvToolsExt.so
libcublas.so.8.0.88 libcusolver.so.8.0 libnppidei.so.8.0 libnvToolsExt.so.1
libcublas_device.a libcusolver.so.8.0.61 libnppidei.so.8.0.61 libnvToolsExt.so.1.0.0
libcublas_static.a libcusolver_static.a libnppif.so libnvblas.so
libcudadevrt.a libcusparse.so libnppif.so.8.0 libnvblas.so.8.0
libcudart.so libcusparse.so.8.0 libnppif.so.8.0.61 libnvblas.so.8.0.61
libcudart.so.8.0 libcusparse.so.8.0.61 libnppig.so libnvblas.so.8.0.88
libcudart.so.8.0.61 libcusparse_static.a libnppig.so.8.0 libnvgraph.so
libcudart_static.a libnppc.so libnppig.so.8.0.61 libnvgraph.so.8.0
libcufft.so libnppc.so.8.0 libnppim.so libnvgraph.so.8.0.61
libcufft.so.8.0 libnppc.so.8.0.61 libnppim.so.8.0 libnvgraph_static.a
libcufft.so.8.0.61 libnppc_static.a libnppim.so.8.0.61 libnvrtc-builtins.so
libcufft_static.a libnppi.so libnppist.so libnvrtc-builtins.so.8.0
libcufftw.so libnppi.so.8.0 libnppist.so.8.0 libnvrtc-builtins.so.8.0.61
libcufftw.so.8.0 libnppi.so.8.0.61 libnppist.so.8.0.61 libnvrtc.so
libcufftw.so.8.0.61 libnppi_static.a libnppisu.so libnvrtc.so.8.0
libcufftw_static.a libnppial.so libnppisu.so.8.0 libnvrtc.so.8.0.61
libcuinj64.so libnppial.so.8.0 libnppisu.so.8.0.61 stubs
libcuinj64.so.8.0 libnppial.so.8.0.61 libnppitc.so
root@b65afb23bc00:/usr/local/cuda/lib64# cp -r ./libcudart.so.8.0 ./libcudart.so.7.5
root@b65afb23bc00:/usr/local/cuda/lib64# ls问题3
pkl转txt文件
python Det_measure_git.py
len of detection is 15
fp [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.1. 1. 1. 2. 2. 3. 4. 4. 4. 4. 5. 6. 6. 6.6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6.6. 6. 7. 7. 7. 7. 8. 8. 9. 9. 9. 9. 10. 10.11. 11. 12. 12. 13. 14. 14. 14. 15. 15. 16. 17. 17. 18.19. 20. 20. 20. 21. 22. 23. 24. 25. 26. 27. 28. 28. 29.29. 29. 30. 31. 32. 33. 33. 34. 35. 35. 36. 37. 38. 39.39. 40. 41. 42. 43. 44. 45. 45. 46. 47. 47. 48. 48. 49.50. 51. 52. 53. 54. 54. 55. 56. 57. 58. 59. 60. 61. 62.63. 64. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75.76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89.90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103.104. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116.117. 118. 119. 119. 120. 121. 122. 123. 124. 125. 126.]
tp [ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 30. 31. 32. 33. 34. 35.36. 37. 38. 39. 40. 41. 42. 43. 44. 44. 45. 45. 45. 46. 47. 48. 48. 48.49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66.66. 67. 68. 69. 69. 70. 70. 71. 72. 73. 73. 74. 74. 75. 75. 76. 76. 76.77. 78. 78. 79. 79. 79. 80. 80. 80. 80. 81. 82. 82. 82. 82. 82. 82. 82.82. 82. 83. 83. 84. 85. 85. 85. 85. 85. 86. 86. 86. 87. 87. 87. 87. 87.88. 88. 88. 88. 88. 88. 88. 89. 89. 89. 90. 90. 91. 91. 91. 91. 91. 91.91. 92. 92. 92. 92. 92. 92. 92. 92. 92. 92. 92. 93. 93. 93. 93. 93. 93.93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93.93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 93. 94.94. 94. 94. 94. 94. 94. 94. 94. 94. 94. 94. 94. 94. 94. 94. 95. 95. 95.95. 95. 95. 95. 95.]
npos 101
the max F is 0.810256410256
the prec is 0.840425531915, and the rec is 0.782178217822
问题四：
root@b65afb23bc00:/home/lyh/mitosis_DET/DeepMitosis/DeepDetection# ./experiments/scripts/faster_rcnn_end2end_12.sh 6 VGG_CNN_M_1024_Scale mitos --set EXP_DIR XX
+ set -e
+ export PYTHONUNBUFFERED=True
+ PYTHONUNBUFFERED=True
+ GPU_ID=6
+ NET=VGG_CNN_M_1024_Scale
+ NET_lc=vgg_cnn_m_1024_scale
+ DATASET=mitos
+ array=($@)
+ len=6
+ EXTRA_ARGS='--set EXP_DIR XX'
+ EXTRA_ARGS_SLUG=--set_EXP_DIR_XX
+ case $DATASET in
+ TRAIN_IMDB=mitos_2012_train
+ TEST_IMDB=mitos_2012_test
+ PT_DIR=mitos
+ ITERS=150000
++ date +%Y-%m-%d_%H-%M-%S
+ LOG=experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-03-01_12-08-15
+ exec
++ tee -a experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-03-01_12-08-15
+ echo Logging output to experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-03-01_12-08-15
Logging output to experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_Scale_--set_EXP_DIR_XX.txt.2020-03-01_12-08-15
+ ./tools/train_net.py --gpu 6 --solver models/mitos/VGG_CNN_M_1024_Scale/faster_rcnn_end2end/solver.prototxt --weights data/imagenet_models/VGG_CNN_M_1024_Scale.v2.caffemodel --imdb mitos_2012_train --iters 150000 --cfg experiments/cfgs/faster_rcnn_end2end_2012.yml --set EXP_DIR XX
Called with args:
Namespace(cfg_file='experiments/cfgs/faster_rcnn_end2end_2012.yml', gpu_id=6, imdb_name='mitos_2012_train', max_iters=150000, pretrained_model='data/imagenet_models/VGG_CNN_M_1024_Scale.v2.caffemodel', randomize=False, set_cfgs=['EXP_DIR', 'XX'], solver='models/mitos/VGG_CNN_M_1024_Scale/faster_rcnn_end2end/solver.prototxt')
Using config:
{'DATA_DIR': '/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/data','DEDUP_BOXES': 0.0625,'EPS': 1e-14,'EXP_DIR': 'XX','GPU_ID': 6,'MATLAB': 'matlab','MODELS_DIR': '/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/models/pascal_voc','PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),'RNG_SEED': 3,'ROOT_DIR': '/home/lyh/mitosis_DET/DeepMitosis/DeepDetection','TEST': {'BBOX_REG': True,'HAS_RPN': True,'MAX_SIZE': 4168,'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': [4168],'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': 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,'HAS_RPN': True,'IMS_PER_BATCH': 1,'MAX_SIZE': 1024,'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': [1024],'SNAPSHOT_INFIX': '','SNAPSHOT_ITERS': 10000,'USE_FLIPPED': True,'USE_PREFETCH': False},'USE_GPU_NMS': True}
Loaded dataset `mitos_2012_train` for training
Set proposal method: gt
Appending horizontally-flipped training examples...wrote gt roidb to /home/lyh/mitosis_DET/DeepMitosis/DeepDetection/data/cache/mitos_2012_train_gt_roidb.pkl
done
Preparing training data...
done
60076 roidb entries
Output will be saved to `/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/output/XX/mitos_2012_train`
Filtered 0 roidb entries: 60076 -> 60076
Computing bounding-box regression targets...
bbox target means:
[[0. 0. 0. 0.][0. 0. 0. 0.]]
[0. 0. 0. 0.]
bbox target stdevs:
[[0.1 0.1 0.2 0.2][0.1 0.1 0.2 0.2]]
[0.1 0.1 0.2 0.2]
Normalizing targets
done
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0301 12:09:03.328075 25742 solver.cpp:48] Initializing solver from parameters:
train_net: "models/mitos/VGG_CNN_M_1024_Scale/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: "vgg_cnn_m_1024_faster_rcnn"
average_loss: 100
I0301 12:09:03.328119 25742 solver.cpp:81] Creating training net from train_net file: models/mitos/VGG_CNN_M_1024_Scale/faster_rcnn_end2end/train.prototxt
I0301 12:09:03.328941 25742 net.cpp:49] Initializing net from parameters:
name: "VGG_CNN_M_1024"
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: 0decay_mult: 0}param {lr_mult: 0decay_mult: 0}convolution_param {num_output: 96kernel_size: 7stride: 2}
}
layer {name: "relu1"type: "ReLU"bottom: "conv1"top: "conv1"
}
layer {name: "norm1"type: "LRN"bottom: "conv1"top: "norm1"lrn_param {local_size: 5alpha: 0.0005beta: 0.75k: 2}
}
layer {name: "pool1"type: "Pooling"bottom: "norm1"top: "pool1"pooling_param {pool: MAXkernel_size: 3stride: 2}
}
layer {name: "conv2"type: "Convolution"bottom: "pool1"top: "conv2"param {lr_mult: 1}param {lr_mult: 2}convolution_param {num_output: 256pad: 1kernel_size: 5stride: 2}
}
layer {name: "relu2"type: "ReLU"bottom: "conv2"top: "conv2"
}
layer {name: "norm2"type: "LRN"bottom: "conv2"top: "norm2"lrn_param {local_size: 5alpha: 0.0005beta: 0.75k: 2}
}
layer {name: "pool2"type: "Pooling"bottom: "norm2"top: "pool2"pooling_param {pool: MAXkernel_size: 3stride: 2}
}
layer {name: "conv3"type: "Convolution"bottom: "pool2"top: "conv3"param {lr_mult: 1}param {lr_mult: 2}convolution_param {num_output: 512pad: 1kernel_size: 3}
}
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: 512pad: 1kernel_size: 3}
}
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: 512pad: 1kernel_size: 3}
}
layer {name: "relu5"type: "ReLU"bottom: "conv5"top: "conv5"
}
layer {name: "rpn_conv/3x3"type: "Convolution"bottom: "conv5"top: "rpn/output"param {lr_mult: 1}param {lr_mult: 2}convolution_param {num_output: 256pad: 1kernel_size: 3stride: 1weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 0}}
}
layer {name: "rpn_relu/3x3"type: "ReLU"bottom: "rpn/output"top: "rpn/output"
}
layer {name: "rpn_cls_score"type: "Convolution"bottom: "rpn/output"top: "rpn_cls_score"param {lr_mult: 1}param {lr_mult: 2}convolution_param {num_output: 18pad: 0kernel_size: 1stride: 1weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 0}}
}
layer {name: "rpn_bbox_pred"type: "Convolution"bottom: "rpn/output"top: "rpn_bbox_pred"param {lr_mult: 1}param {lr_mult: 2}convolution_param {num_output: 36pad: 0kernel_size: 1stride: 1weight_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: 0dim: 2dim: -1dim: 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: "rpnS.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: 1propagate_down: truepropagate_down: falseloss_param {ignore_label: -1normalize: 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: 1smooth_l1_loss_param {sigma: 3}
}
layer {name: "rpn_cls_prob"type: "Softmax"bottom: "rpn_cls_score_reshape"top: "rpn_cls_prob"
}
layer {name: "rpn_cls_prob_reshape"type: "Reshape"bottom: "rpn_cls_prob"top: "rpn_cls_prob_reshape"reshape_param {shape {dim: 0dim: 18dim: -1dim: 0}}
}
layer {name: "proposal"type: "Python"bottom: "rpn_cls_prob_reshape"bottom: "rpn_bbox_pred"bottom: "im_info"top: "rpn_rois"python_param {module: "rpnS.proposal_layer"layer: "ProposalLayer"param_str: "\'feat_stride\': 16"}
}
layer {name: "roi-data"type: "Python"bottom: "rpn_rois"bottom: "gt_boxes"top: "rois"top: "labels"top: "bbox_targets"top: "bbox_inside_weights"top: "bbox_outside_weights"python_param {module: "rpnS.proposal_target_layer"layer: "ProposalTargetLayer"param_str: "\'num_classes\': 2"}
}
layer {name: "roi_pool5"type: "ROIPooling"bottom: "conv5"bottom: "rois"top: "pool5"roi_pooling_param {pooled_h: 6pooled_w: 6spatial_scale: 0.0625}
}
layer {name: "fc6"type: "InnerProduct"bottom: "pool5"top: "fc6"param {lr_mult: 1}param {lr_mult: 2}inner_product_param {num_output: 4096}
}
layer {name: "relu6"type: "ReLU"bottom: "fc6"top: "fc6"
}
layer {name: "drop6"type: "Dropout"bottom: "fc6"top: "fc6"dropout_param {dropout_ratio: 0.5}
}
layer {name: "fc7"type: "InnerProduct"bottom: "fc6"top: "fc7"param {lr_mult: 1}param {lr_mult: 2}inner_product_param {num_output: 1024}
}
layer {name: "relu7"type: "ReLU"bottom: "fc7"top: "fc7"
}
layer {name: "drop7"type: "Dropout"bottom: "fc7"top: "fc7"dropout_param {dropout_ratio: 0.5}
}
layer {name: "cls_score"type: "InnerProduct"bottom: "fc7"top: "cls_score"param {lr_mult: 1}param {lr_mult: 2}inner_product_param {num_output: 2weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"value: 0}}
}
layer {name: "bbox_pred"type: "InnerProduct"bottom: "fc7"top: "bbox_pred"param {lr_mult: 1}param {lr_mult: 2}inner_product_param {num_output: 8weight_filler {type: "gaussian"std: 0.001}bias_filler {type: "constant"value: 0}}
}
layer {name: "loss_cls"type: "SoftmaxWithLoss"bottom: "cls_score"bottom: "labels"top: "loss_cls"loss_weight: 1propagate_down: truepropagate_down: false
}
layer {name: "loss_bbox"type: "SmoothL1Loss"bottom: "bbox_pred"bottom: "bbox_targets"bottom: "bbox_inside_weights"bottom: "bbox_outside_weights"top: "loss_bbox"loss_weight: 1
}
I0301 12:09:03.329243 25742 layer_factory.hpp:77] Creating layer input-data
I0301 12:09:03.409370 25742 net.cpp:106] Creating Layer input-data
I0301 12:09:03.415974 25742 net.cpp:411] input-data -> data
I0301 12:09:03.416013 25742 net.cpp:411] input-data -> im_info
I0301 12:09:03.416020 25742 net.cpp:411] input-data -> gt_boxes
RoiDataLayer: name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1}
I0301 12:09:03.574605 25742 net.cpp:150] Setting up input-data
I0301 12:09:03.574633 25742 net.cpp:157] Top shape: 1 3 1024 1024 (3145728)
I0301 12:09:03.574640 25742 net.cpp:157] Top shape: 1 3 (3)
I0301 12:09:03.574645 25742 net.cpp:157] Top shape: 1 4 (4)
I0301 12:09:03.574651 25742 net.cpp:165] Memory required for data: 12582940
I0301 12:09:03.574661 25742 layer_factory.hpp:77] Creating layer data_input-data_0_split
I0301 12:09:03.574677 25742 net.cpp:106] Creating Layer data_input-data_0_split
I0301 12:09:03.574692 25742 net.cpp:454] data_input-data_0_split <- data
I0301 12:09:03.574703 25742 net.cpp:411] data_input-data_0_split -> data_input-data_0_split_0
I0301 12:09:03.574726 25742 net.cpp:411] data_input-data_0_split -> data_input-data_0_split_1
I0301 12:09:03.574796 25742 net.cpp:150] Setting up data_input-data_0_split
I0301 12:09:03.574807 25742 net.cpp:157] Top shape: 1 3 1024 1024 (3145728)
I0301 12:09:03.574816 25742 net.cpp:157] Top shape: 1 3 1024 1024 (3145728)
I0301 12:09:03.574848 25742 net.cpp:165] Memory required for data: 37748764
I0301 12:09:03.574867 25742 layer_factory.hpp:77] Creating layer im_info_input-data_1_split
I0301 12:09:03.574889 25742 net.cpp:106] Creating Layer im_info_input-data_1_split
I0301 12:09:03.574908 25742 net.cpp:454] im_info_input-data_1_split <- im_info
I0301 12:09:03.574928 25742 net.cpp:411] im_info_input-data_1_split -> im_info_input-data_1_split_0
I0301 12:09:03.574950 25742 net.cpp:411] im_info_input-data_1_split -> im_info_input-data_1_split_1
I0301 12:09:03.575040 25742 net.cpp:150] Setting up im_info_input-data_1_split
I0301 12:09:03.575053 25742 net.cpp:157] Top shape: 1 3 (3)
I0301 12:09:03.575059 25742 net.cpp:157] Top shape: 1 3 (3)
I0301 12:09:03.575064 25742 net.cpp:165] Memory required for data: 37748788
I0301 12:09:03.575069 25742 layer_factory.hpp:77] Creating layer gt_boxes_input-data_2_split
I0301 12:09:03.575078 25742 net.cpp:106] Creating Layer gt_boxes_input-data_2_split
I0301 12:09:03.575083 25742 net.cpp:454] gt_boxes_input-data_2_split <- gt_boxes
I0301 12:09:03.575091 25742 net.cpp:411] gt_boxes_input-data_2_split -> gt_boxes_input-data_2_split_0
I0301 12:09:03.575101 25742 net.cpp:411] gt_boxes_input-data_2_split -> gt_boxes_input-data_2_split_1
I0301 12:09:03.575178 25742 net.cpp:150] Setting up gt_boxes_input-data_2_split
I0301 12:09:03.575191 25742 net.cpp:157] Top shape: 1 4 (4)
I0301 12:09:03.575202 25742 net.cpp:157] Top shape: 1 4 (4)
I0301 12:09:03.575209 25742 net.cpp:165] Memory required for data: 37748820
I0301 12:09:03.575215 25742 layer_factory.hpp:77] Creating layer conv1
I0301 12:09:03.575246 25742 net.cpp:106] Creating Layer conv1
I0301 12:09:03.575255 25742 net.cpp:454] conv1 <- data_input-data_0_split_0
I0301 12:09:03.575264 25742 net.cpp:411] conv1 -> conv1
I0301 12:09:07.443814 25742 net.cpp:150] Setting up conv1
I0301 12:09:07.443861 25742 net.cpp:157] Top shape: 1 96 509 509 (24871776)
I0301 12:09:07.443864 25742 net.cpp:165] Memory required for data: 137235924
I0301 12:09:07.443884 25742 layer_factory.hpp:77] Creating layer relu1
I0301 12:09:07.443898 25742 net.cpp:106] Creating Layer relu1
I0301 12:09:07.443903 25742 net.cpp:454] relu1 <- conv1
I0301 12:09:07.443910 25742 net.cpp:397] relu1 -> conv1 (in-place)
I0301 12:09:07.445688 25742 net.cpp:150] Setting up relu1
I0301 12:09:07.445703 25742 net.cpp:157] Top shape: 1 96 509 509 (24871776)
I0301 12:09:07.445708 25742 net.cpp:165] Memory required for data: 236723028
I0301 12:09:07.445711 25742 layer_factory.hpp:77] Creating layer norm1
I0301 12:09:07.445721 25742 net.cpp:106] Creating Layer norm1
I0301 12:09:07.445729 25742 net.cpp:454] norm1 <- conv1
I0301 12:09:07.445737 25742 net.cpp:411] norm1 -> norm1
I0301 12:09:07.447317 25742 net.cpp:150] Setting up norm1
I0301 12:09:07.447327 25742 net.cpp:157] Top shape: 1 96 509 509 (24871776)
I0301 12:09:07.447330 25742 net.cpp:165] Memory required for data: 336210132
I0301 12:09:07.447335 25742 layer_factory.hpp:77] Creating layer pool1
I0301 12:09:07.447343 25742 net.cpp:106] Creating Layer pool1
I0301 12:09:07.447348 25742 net.cpp:454] pool1 <- norm1
I0301 12:09:07.447355 25742 net.cpp:411] pool1 -> pool1
I0301 12:09:07.447450 25742 net.cpp:150] Setting up pool1
I0301 12:09:07.447463 25742 net.cpp:157] Top shape: 1 96 254 254 (6193536)
I0301 12:09:07.447468 25742 net.cpp:165] Memory required for data: 360984276
I0301 12:09:07.447474 25742 layer_factory.hpp:77] Creating layer conv2
I0301 12:09:07.447495 25742 net.cpp:106] Creating Layer conv2
I0301 12:09:07.447501 25742 net.cpp:454] conv2 <- pool1
I0301 12:09:07.447551 25742 net.cpp:411] conv2 -> conv2
I0301 12:09:07.457021 25742 net.cpp:150] Setting up conv2
I0301 12:09:07.457042 25742 net.cpp:157] Top shape: 1 256 126 126 (4064256)
I0301 12:09:07.457046 25742 net.cpp:165] Memory required for data: 377241300
I0301 12:09:07.457058 25742 layer_factory.hpp:77] Creating layer relu2
I0301 12:09:07.457067 25742 net.cpp:106] Creating Layer relu2
I0301 12:09:07.457072 25742 net.cpp:454] relu2 <- conv2
I0301 12:09:07.457078 25742 net.cpp:397] relu2 -> conv2 (in-place)
I0301 12:09:07.460460 25742 net.cpp:150] Setting up relu2
I0301 12:09:07.460474 25742 net.cpp:157] Top shape: 1 256 126 126 (4064256)
I0301 12:09:07.460477 25742 net.cpp:165] Memory required for data: 393498324
I0301 12:09:07.460481 25742 layer_factory.hpp:77] Creating layer norm2
I0301 12:09:07.460494 25742 net.cpp:106] Creating Layer norm2
I0301 12:09:07.460515 25742 net.cpp:454] norm2 <- conv2
I0301 12:09:07.460526 25742 net.cpp:411] norm2 -> norm2
I0301 12:09:07.461015 25742 net.cpp:150] Setting up norm2
I0301 12:09:07.461026 25742 net.cpp:157] Top shape: 1 256 126 126 (4064256)
I0301 12:09:07.461030 25742 net.cpp:165] Memory required for data: 409755348
I0301 12:09:07.461033 25742 layer_factory.hpp:77] Creating layer pool2
I0301 12:09:07.461045 25742 net.cpp:106] Creating Layer pool2
I0301 12:09:07.461050 25742 net.cpp:454] pool2 <- norm2
I0301 12:09:07.461058 25742 net.cpp:411] pool2 -> pool2
I0301 12:09:07.461153 25742 net.cpp:150] Setting up pool2
I0301 12:09:07.461165 25742 net.cpp:157] Top shape: 1 256 63 63 (1016064)
I0301 12:09:07.461169 25742 net.cpp:165] Memory required for data: 413819604
I0301 12:09:07.461174 25742 layer_factory.hpp:77] Creating layer conv3
I0301 12:09:07.461189 25742 net.cpp:106] Creating Layer conv3
I0301 12:09:07.461194 25742 net.cpp:454] conv3 <- pool2
I0301 12:09:07.461205 25742 net.cpp:411] conv3 -> conv3
I0301 12:09:07.488526 25742 net.cpp:150] Setting up conv3
I0301 12:09:07.488564 25742 net.cpp:157] Top shape: 1 512 63 63 (2032128)
I0301 12:09:07.488570 25742 net.cpp:165] Memory required for data: 421948116
I0301 12:09:07.488610 25742 layer_factory.hpp:77] Creating layer relu3
I0301 12:09:07.488631 25742 net.cpp:106] Creating Layer relu3
I0301 12:09:07.488641 25742 net.cpp:454] relu3 <- conv3
I0301 12:09:07.488653 25742 net.cpp:397] relu3 -> conv3 (in-place)
I0301 12:09:07.499197 25742 net.cpp:150] Setting up relu3
I0301 12:09:07.499238 25742 net.cpp:157] Top shape: 1 512 63 63 (2032128)
I0301 12:09:07.499244 25742 net.cpp:165] Memory required for data: 430076628
I0301 12:09:07.499254 25742 layer_factory.hpp:77] Creating layer conv4
I0301 12:09:07.499286 25742 net.cpp:106] Creating Layer conv4
I0301 12:09:07.499296 25742 net.cpp:454] conv4 <- conv3
I0301 12:09:07.499313 25742 net.cpp:411] conv4 -> conv4
I0301 12:09:07.537977 25742 net.cpp:150] Setting up conv4
I0301 12:09:07.538018 25742 net.cpp:157] Top shape: 1 512 63 63 (2032128)
I0301 12:09:07.538024 25742 net.cpp:165] Memory required for data: 438205140
I0301 12:09:07.538059 25742 layer_factory.hpp:77] Creating layer relu4
I0301 12:09:07.538097 25742 net.cpp:106] Creating Layer relu4
I0301 12:09:07.538108 25742 net.cpp:454] relu4 <- conv4
I0301 12:09:07.538122 25742 net.cpp:397] relu4 -> conv4 (in-place)
I0301 12:09:07.539922 25742 net.cpp:150] Setting up relu4
I0301 12:09:07.539942 25742 net.cpp:157] Top shape: 1 512 63 63 (2032128)
I0301 12:09:07.539945 25742 net.cpp:165] Memory required for data: 446333652
I0301 12:09:07.539952 25742 layer_factory.hpp:77] Creating layer conv5
I0301 12:09:07.539978 25742 net.cpp:106] Creating Layer conv5
I0301 12:09:07.540010 25742 net.cpp:454] conv5 <- conv4
I0301 12:09:07.540036 25742 net.cpp:411] conv5 -> conv5
I0301 12:09:07.564262 25742 net.cpp:150] Setting up conv5
I0301 12:09:07.564296 25742 net.cpp:157] Top shape: 1 512 63 63 (2032128)
I0301 12:09:07.564302 25742 net.cpp:165] Memory required for data: 454462164
I0301 12:09:07.564327 25742 layer_factory.hpp:77] Creating layer relu5
I0301 12:09:07.564347 25742 net.cpp:106] Creating Layer relu5
I0301 12:09:07.564370 25742 net.cpp:454] relu5 <- conv5
I0301 12:09:07.564380 25742 net.cpp:397] relu5 -> conv5 (in-place)
I0301 12:09:07.566282 25742 net.cpp:150] Setting up relu5
I0301 12:09:07.566299 25742 net.cpp:157] Top shape: 1 512 63 63 (2032128)
I0301 12:09:07.566303 25742 net.cpp:165] Memory required for data: 462590676
I0301 12:09:07.566308 25742 layer_factory.hpp:77] Creating layer conv5_relu5_0_split
I0301 12:09:07.566319 25742 net.cpp:106] Creating Layer conv5_relu5_0_split
I0301 12:09:07.566324 25742 net.cpp:454] conv5_relu5_0_split <- conv5
I0301 12:09:07.566337 25742 net.cpp:411] conv5_relu5_0_split -> conv5_relu5_0_split_0
I0301 12:09:07.566349 25742 net.cpp:411] conv5_relu5_0_split -> conv5_relu5_0_split_1
I0301 12:09:07.566452 25742 net.cpp:150] Setting up conv5_relu5_0_split
I0301 12:09:07.566464 25742 net.cpp:157] Top shape: 1 512 63 63 (2032128)
I0301 12:09:07.566471 25742 net.cpp:157] Top shape: 1 512 63 63 (2032128)
I0301 12:09:07.566474 25742 net.cpp:165] Memory required for data: 478847700
I0301 12:09:07.566479 25742 layer_factory.hpp:77] Creating layer rpn_conv/3x3
I0301 12:09:07.566502 25742 net.cpp:106] Creating Layer rpn_conv/3x3
I0301 12:09:07.566509 25742 net.cpp:454] rpn_conv/3x3 <- conv5_relu5_0_split_0
I0301 12:09:07.566521 25742 net.cpp:411] rpn_conv/3x3 -> rpn/output
I0301 12:09:07.602360 25742 net.cpp:150] Setting up rpn_conv/3x3
I0301 12:09:07.602391 25742 net.cpp:157] Top shape: 1 256 63 63 (1016064)
I0301 12:09:07.602396 25742 net.cpp:165] Memory required for data: 482911956
I0301 12:09:07.602429 25742 layer_factory.hpp:77] Creating layer rpn_relu/3x3
I0301 12:09:07.602445 25742 net.cpp:106] Creating Layer rpn_relu/3x3
I0301 12:09:07.602454 25742 net.cpp:454] rpn_relu/3x3 <- rpn/output
I0301 12:09:07.602470 25742 net.cpp:397] rpn_relu/3x3 -> rpn/output (in-place)
I0301 12:09:07.602630 25742 net.cpp:150] Setting up rpn_relu/3x3
I0301 12:09:07.602640 25742 net.cpp:157] Top shape: 1 256 63 63 (1016064)
I0301 12:09:07.602643 25742 net.cpp:165] Memory required for data: 486976212
I0301 12:09:07.602648 25742 layer_factory.hpp:77] Creating layer rpn/output_rpn_relu/3x3_0_split
I0301 12:09:07.602654 25742 net.cpp:106] Creating Layer rpn/output_rpn_relu/3x3_0_split
I0301 12:09:07.602658 25742 net.cpp:454] rpn/output_rpn_relu/3x3_0_split <- rpn/output
I0301 12:09:07.602664 25742 net.cpp:411] rpn/output_rpn_relu/3x3_0_split -> rpn/output_rpn_relu/3x3_0_split_0
I0301 12:09:07.602674 25742 net.cpp:411] rpn/output_rpn_relu/3x3_0_split -> rpn/output_rpn_relu/3x3_0_split_1
I0301 12:09:07.602744 25742 net.cpp:150] Setting up rpn/output_rpn_relu/3x3_0_split
I0301 12:09:07.602751 25742 net.cpp:157] Top shape: 1 256 63 63 (1016064)
I0301 12:09:07.602753 25742 net.cpp:157] Top shape: 1 256 63 63 (1016064)
I0301 12:09:07.602756 25742 net.cpp:165] Memory required for data: 495104724
I0301 12:09:07.602759 25742 layer_factory.hpp:77] Creating layer rpn_cls_score
I0301 12:09:07.602777 25742 net.cpp:106] Creating Layer rpn_cls_score
I0301 12:09:07.602783 25742 net.cpp:454] rpn_cls_score <- rpn/output_rpn_relu/3x3_0_split_0
I0301 12:09:07.602797 25742 net.cpp:411] rpn_cls_score -> rpn_cls_score
I0301 12:09:07.609822 25742 net.cpp:150] Setting up rpn_cls_score
I0301 12:09:07.609865 25742 net.cpp:157] Top shape: 1 18 63 63 (71442)
I0301 12:09:07.609869 25742 net.cpp:165] Memory required for data: 495390492
I0301 12:09:07.609882 25742 layer_factory.hpp:77] Creating layer rpn_cls_score_rpn_cls_score_0_split
I0301 12:09:07.609912 25742 net.cpp:106] Creating Layer rpn_cls_score_rpn_cls_score_0_split
I0301 12:09:07.609916 25742 net.cpp:454] rpn_cls_score_rpn_cls_score_0_split <- rpn_cls_score
I0301 12:09:07.609943 25742 net.cpp:411] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_0
I0301 12:09:07.609956 25742 net.cpp:411] rpn_cls_score_rpn_cls_score_0_split -> rpn_cls_score_rpn_cls_score_0_split_1
I0301 12:09:07.610019 25742 net.cpp:150] Setting up rpn_cls_score_rpn_cls_score_0_split
I0301 12:09:07.610028 25742 net.cpp:157] Top shape: 1 18 63 63 (71442)
I0301 12:09:07.610031 25742 net.cpp:157] Top shape: 1 18 63 63 (71442)
I0301 12:09:07.610034 25742 net.cpp:165] Memory required for data: 495962028
I0301 12:09:07.610038 25742 layer_factory.hpp:77] Creating layer rpn_bbox_pred
I0301 12:09:07.610059 25742 net.cpp:106] Creating Layer rpn_bbox_pred
I0301 12:09:07.610064 25742 net.cpp:454] rpn_bbox_pred <- rpn/output_rpn_relu/3x3_0_split_1
I0301 12:09:07.610070 25742 net.cpp:411] rpn_bbox_pred -> rpn_bbox_pred
I0301 12:09:07.615131 25742 net.cpp:150] Setting up rpn_bbox_pred
I0301 12:09:07.615149 25742 net.cpp:157] Top shape: 1 36 63 63 (142884)
I0301 12:09:07.615164 25742 net.cpp:165] Memory required for data: 496533564
I0301 12:09:07.615173 25742 layer_factory.hpp:77] Creating layer rpn_bbox_pred_rpn_bbox_pred_0_split
I0301 12:09:07.615185 25742 net.cpp:106] Creating Layer rpn_bbox_pred_rpn_bbox_pred_0_split
I0301 12:09:07.615188 25742 net.cpp:454] rpn_bbox_pred_rpn_bbox_pred_0_split <- rpn_bbox_pred
I0301 12:09:07.615195 25742 net.cpp:411] rpn_bbox_pred_rpn_bbox_pred_0_split -> rpn_bbox_pred_rpn_bbox_pred_0_split_0
I0301 12:09:07.615219 25742 net.cpp:411] rpn_bbox_pred_rpn_bbox_pred_0_split -> rpn_bbox_pred_rpn_bbox_pred_0_split_1
I0301 12:09:07.615304 25742 net.cpp:150] Setting up rpn_bbox_pred_rpn_bbox_pred_0_split
I0301 12:09:07.615314 25742 net.cpp:157] Top shape: 1 36 63 63 (142884)
I0301 12:09:07.615317 25742 net.cpp:157] Top shape: 1 36 63 63 (142884)
I0301 12:09:07.615320 25742 net.cpp:165] Memory required for data: 497676636
I0301 12:09:07.615324 25742 layer_factory.hpp:77] Creating layer rpn_cls_score_reshape
I0301 12:09:07.615334 25742 net.cpp:106] Creating Layer rpn_cls_score_reshape
I0301 12:09:07.615337 25742 net.cpp:454] rpn_cls_score_reshape <- rpn_cls_score_rpn_cls_score_0_split_0
I0301 12:09:07.615344 25742 net.cpp:411] rpn_cls_score_reshape -> rpn_cls_score_reshape
I0301 12:09:07.615384 25742 net.cpp:150] Setting up rpn_cls_score_reshape
I0301 12:09:07.615392 25742 net.cpp:157] Top shape: 1 2 567 63 (71442)
I0301 12:09:07.615396 25742 net.cpp:165] Memory required for data: 497962404
I0301 12:09:07.615401 25742 layer_factory.hpp:77] Creating layer rpn_cls_score_reshape_rpn_cls_score_reshape_0_split
I0301 12:09:07.615409 25742 net.cpp:106] Creating Layer rpn_cls_score_reshape_rpn_cls_score_reshape_0_split
I0301 12:09:07.615417 25742 net.cpp:454] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split <- rpn_cls_score_reshape
I0301 12:09:07.615427 25742 net.cpp:411] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split -> rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_0
I0301 12:09:07.615439 25742 net.cpp:411] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split -> rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_1
I0301 12:09:07.615551 25742 net.cpp:150] Setting up rpn_cls_score_reshape_rpn_cls_score_reshape_0_split
I0301 12:09:07.615593 25742 net.cpp:157] Top shape: 1 2 567 63 (71442)
I0301 12:09:07.615613 25742 net.cpp:157] Top shape: 1 2 567 63 (71442)
I0301 12:09:07.615618 25742 net.cpp:165] Memory required for data: 498533940
I0301 12:09:07.615623 25742 layer_factory.hpp:77] Creating layer rpn-data
I0301 12:09:07.651859 25742 net.cpp:106] Creating Layer rpn-data
I0301 12:09:07.651893 25742 net.cpp:454] rpn-data <- rpn_cls_score_rpn_cls_score_0_split_1
I0301 12:09:07.651911 25742 net.cpp:454] rpn-data <- gt_boxes_input-data_2_split_0
I0301 12:09:07.651918 25742 net.cpp:454] rpn-data <- im_info_input-data_1_split_0
I0301 12:09:07.651931 25742 net.cpp:454] rpn-data <- data_input-data_0_split_1
I0301 12:09:07.651943 25742 net.cpp:411] rpn-data -> rpn_labels
I0301 12:09:07.651978 25742 net.cpp:411] rpn-data -> rpn_bbox_targets
I0301 12:09:07.651996 25742 net.cpp:411] rpn-data -> rpn_bbox_inside_weights
I0301 12:09:07.652011 25742 net.cpp:411] rpn-data -> rpn_bbox_outside_weights
I0301 12:09:07.654270 25742 net.cpp:150] Setting up rpn-data
I0301 12:09:07.654297 25742 net.cpp:157] Top shape: 1 1 567 63 (35721)
I0301 12:09:07.654309 25742 net.cpp:157] Top shape: 1 36 63 63 (142884)
I0301 12:09:07.654316 25742 net.cpp:157] Top shape: 1 36 63 63 (142884)
I0301 12:09:07.654323 25742 net.cpp:157] Top shape: 1 36 63 63 (142884)
I0301 12:09:07.654328 25742 net.cpp:165] Memory required for data: 500391432
I0301 12:09:07.654336 25742 layer_factory.hpp:77] Creating layer rpn_loss_cls
I0301 12:09:07.654350 25742 net.cpp:106] Creating Layer rpn_loss_cls
I0301 12:09:07.654361 25742 net.cpp:454] rpn_loss_cls <- rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_0
I0301 12:09:07.654372 25742 net.cpp:454] rpn_loss_cls <- rpn_labels
I0301 12:09:07.654384 25742 net.cpp:411] rpn_loss_cls -> rpn_cls_loss
I0301 12:09:07.654400 25742 layer_factory.hpp:77] Creating layer rpn_loss_cls
I0301 12:09:07.656189 25742 net.cpp:150] Setting up rpn_loss_cls
I0301 12:09:07.656210 25742 net.cpp:157] Top shape: (1)
I0301 12:09:07.656219 25742 net.cpp:160] with loss weight 1
I0301 12:09:07.656255 25742 net.cpp:165] Memory required for data: 500391436
I0301 12:09:07.656262 25742 layer_factory.hpp:77] Creating layer rpn_loss_bbox
I0301 12:09:07.656275 25742 net.cpp:106] Creating Layer rpn_loss_bbox
I0301 12:09:07.656282 25742 net.cpp:454] rpn_loss_bbox <- rpn_bbox_pred_rpn_bbox_pred_0_split_0
I0301 12:09:07.656291 25742 net.cpp:454] rpn_loss_bbox <- rpn_bbox_targets
I0301 12:09:07.656298 25742 net.cpp:454] rpn_loss_bbox <- rpn_bbox_inside_weights
I0301 12:09:07.656306 25742 net.cpp:454] rpn_loss_bbox <- rpn_bbox_outside_weights
I0301 12:09:07.656316 25742 net.cpp:411] rpn_loss_bbox -> rpn_loss_bbox
I0301 12:09:07.660487 25742 net.cpp:150] Setting up rpn_loss_bbox
I0301 12:09:07.660511 25742 net.cpp:157] Top shape: (1)
I0301 12:09:07.660521 25742 net.cpp:160] with loss weight 1
I0301 12:09:07.660533 25742 net.cpp:165] Memory required for data: 500391440
I0301 12:09:07.660540 25742 layer_factory.hpp:77] Creating layer rpn_cls_prob
I0301 12:09:07.660552 25742 net.cpp:106] Creating Layer rpn_cls_prob
I0301 12:09:07.660562 25742 net.cpp:454] rpn_cls_prob <- rpn_cls_score_reshape_rpn_cls_score_reshape_0_split_1
I0301 12:09:07.660576 25742 net.cpp:411] rpn_cls_prob -> rpn_cls_prob
I0301 12:09:07.660893 25742 net.cpp:150] Setting up rpn_cls_prob
I0301 12:09:07.660912 25742 net.cpp:157] Top shape: 1 2 567 63 (71442)
I0301 12:09:07.660920 25742 net.cpp:165] Memory required for data: 500677208
I0301 12:09:07.660926 25742 layer_factory.hpp:77] Creating layer rpn_cls_prob_reshape
I0301 12:09:07.660951 25742 net.cpp:106] Creating Layer rpn_cls_prob_reshape
I0301 12:09:07.660961 25742 net.cpp:454] rpn_cls_prob_reshape <- rpn_cls_prob
I0301 12:09:07.660975 25742 net.cpp:411] rpn_cls_prob_reshape -> rpn_cls_prob_reshape
I0301 12:09:07.661037 25742 net.cpp:150] Setting up rpn_cls_prob_reshape
I0301 12:09:07.661100 25742 net.cpp:157] Top shape: 1 18 63 63 (71442)
I0301 12:09:07.661134 25742 net.cpp:165] Memory required for data: 500962976
I0301 12:09:07.661156 25742 layer_factory.hpp:77] Creating layer proposal
I0301 12:09:07.699777 25742 net.cpp:106] Creating Layer proposal
I0301 12:09:07.700019 25742 net.cpp:454] proposal <- rpn_cls_prob_reshape
I0301 12:09:07.700074 25742 net.cpp:454] proposal <- rpn_bbox_pred_rpn_bbox_pred_0_split_1
I0301 12:09:07.700119 25742 net.cpp:454] proposal <- im_info_input-data_1_split_1
I0301 12:09:07.700166 25742 net.cpp:411] proposal -> rpn_rois
I0301 12:09:07.708047 25742 net.cpp:150] Setting up proposal
I0301 12:09:07.708212 25742 net.cpp:157] Top shape: 1 5 (5)
I0301 12:09:07.708245 25742 net.cpp:165] Memory required for data: 500962996
I0301 12:09:07.708283 25742 layer_factory.hpp:77] Creating layer roi-data
I0301 12:09:07.712116 25742 net.cpp:106] Creating Layer roi-data
I0301 12:09:07.712198 25742 net.cpp:454] roi-data <- rpn_rois
I0301 12:09:07.712234 25742 net.cpp:454] roi-data <- gt_boxes_input-data_2_split_1
I0301 12:09:07.712265 25742 net.cpp:411] roi-data -> rois
I0301 12:09:07.712302 25742 net.cpp:411] roi-data -> labels
I0301 12:09:07.712334 25742 net.cpp:411] roi-data -> bbox_targets
I0301 12:09:07.712376 25742 net.cpp:411] roi-data -> bbox_inside_weights
I0301 12:09:07.712421 25742 net.cpp:411] roi-data -> bbox_outside_weights
I0301 12:09:07.713608 25742 net.cpp:150] Setting up roi-data
I0301 12:09:07.713639 25742 net.cpp:157] Top shape: 1 5 (5)
I0301 12:09:07.713647 25742 net.cpp:157] Top shape: 1 1 (1)
I0301 12:09:07.713654 25742 net.cpp:157] Top shape: 1 8 (8)
I0301 12:09:07.713660 25742 net.cpp:157] Top shape: 1 8 (8)
I0301 12:09:07.713714 25742 net.cpp:157] Top shape: 1 8 (8)
I0301 12:09:07.713737 25742 net.cpp:165] Memory required for data: 500963116
I0301 12:09:07.713796 25742 layer_factory.hpp:77] Creating layer roi_pool5
I0301 12:09:07.713845 25742 net.cpp:106] Creating Layer roi_pool5
I0301 12:09:07.713871 25742 net.cpp:454] roi_pool5 <- conv5_relu5_0_split_1
I0301 12:09:07.713898 25742 net.cpp:454] roi_pool5 <- rois
I0301 12:09:07.713927 25742 net.cpp:411] roi_pool5 -> pool5
I0301 12:09:07.713971 25742 roi_pooling_layer.cpp:30] Spatial scale: 0.0625
I0301 12:09:07.714128 25742 net.cpp:150] Setting up roi_pool5
I0301 12:09:07.714161 25742 net.cpp:157] Top shape: 1 512 6 6 (18432)
I0301 12:09:07.714184 25742 net.cpp:165] Memory required for data: 501036844
I0301 12:09:07.714218 25742 layer_factory.hpp:77] Creating layer fc6
I0301 12:09:07.714249 25742 net.cpp:106] Creating Layer fc6
I0301 12:09:07.714299 25742 net.cpp:454] fc6 <- pool5
I0301 12:09:07.714327 25742 net.cpp:411] fc6 -> fc6
I0301 12:09:08.001680 25742 net.cpp:150] Setting up fc6
I0301 12:09:08.001716 25742 net.cpp:157] Top shape: 1 4096 (4096)
I0301 12:09:08.001720 25742 net.cpp:165] Memory required for data: 501053228
I0301 12:09:08.001741 25742 layer_factory.hpp:77] Creating layer relu6
I0301 12:09:08.001754 25742 net.cpp:106] Creating Layer relu6
I0301 12:09:08.001762 25742 net.cpp:454] relu6 <- fc6
I0301 12:09:08.001772 25742 net.cpp:397] relu6 -> fc6 (in-place)
I0301 12:09:08.003571 25742 net.cpp:150] Setting up relu6
I0301 12:09:08.003623 25742 net.cpp:157] Top shape: 1 4096 (4096)
I0301 12:09:08.003630 25742 net.cpp:165] Memory required for data: 501069612
I0301 12:09:08.003638 25742 layer_factory.hpp:77] Creating layer drop6
I0301 12:09:08.003654 25742 net.cpp:106] Creating Layer drop6
I0301 12:09:08.003664 25742 net.cpp:454] drop6 <- fc6
I0301 12:09:08.003675 25742 net.cpp:397] drop6 -> fc6 (in-place)
I0301 12:09:08.003747 25742 net.cpp:150] Setting up drop6
I0301 12:09:08.003756 25742 net.cpp:157] Top shape: 1 4096 (4096)
I0301 12:09:08.003762 25742 net.cpp:165] Memory required for data: 501085996
I0301 12:09:08.003767 25742 layer_factory.hpp:77] Creating layer fc7
I0301 12:09:08.003779 25742 net.cpp:106] Creating Layer fc7
I0301 12:09:08.003784 25742 net.cpp:454] fc7 <- fc6
I0301 12:09:08.003794 25742 net.cpp:411] fc7 -> fc7
I0301 12:09:08.025454 25742 net.cpp:150] Setting up fc7
I0301 12:09:08.025508 25742 net.cpp:157] Top shape: 1 1024 (1024)
I0301 12:09:08.025511 25742 net.cpp:165] Memory required for data: 501090092
I0301 12:09:08.025523 25742 layer_factory.hpp:77] Creating layer relu7
I0301 12:09:08.025537 25742 net.cpp:106] Creating Layer relu7
I0301 12:09:08.025544 25742 net.cpp:454] relu7 <- fc7
I0301 12:09:08.025557 25742 net.cpp:397] relu7 -> fc7 (in-place)
I0301 12:09:08.025746 25742 net.cpp:150] Setting up relu7
I0301 12:09:08.025758 25742 net.cpp:157] Top shape: 1 1024 (1024)
I0301 12:09:08.025763 25742 net.cpp:165] Memory required for data: 501094188
I0301 12:09:08.025766 25742 layer_factory.hpp:77] Creating layer drop7
I0301 12:09:08.025777 25742 net.cpp:106] Creating Layer drop7
I0301 12:09:08.025815 25742 net.cpp:454] drop7 <- fc7
I0301 12:09:08.025820 25742 net.cpp:397] drop7 -> fc7 (in-place)
I0301 12:09:08.025876 25742 net.cpp:150] Setting up drop7
I0301 12:09:08.025887 25742 net.cpp:157] Top shape: 1 1024 (1024)
I0301 12:09:08.025892 25742 net.cpp:165] Memory required for data: 501098284
I0301 12:09:08.025897 25742 layer_factory.hpp:77] Creating layer fc7_drop7_0_split
I0301 12:09:08.025923 25742 net.cpp:106] Creating Layer fc7_drop7_0_split
I0301 12:09:08.025933 25742 net.cpp:454] fc7_drop7_0_split <- fc7
I0301 12:09:08.025940 25742 net.cpp:411] fc7_drop7_0_split -> fc7_drop7_0_split_0
I0301 12:09:08.025947 25742 net.cpp:411] fc7_drop7_0_split -> fc7_drop7_0_split_1
I0301 12:09:08.026029 25742 net.cpp:150] Setting up fc7_drop7_0_split
I0301 12:09:08.026036 25742 net.cpp:157] Top shape: 1 1024 (1024)
I0301 12:09:08.026039 25742 net.cpp:157] Top shape: 1 1024 (1024)
I0301 12:09:08.026042 25742 net.cpp:165] Memory required for data: 501106476
I0301 12:09:08.026046 25742 layer_factory.hpp:77] Creating layer cls_score
I0301 12:09:08.026058 25742 net.cpp:106] Creating Layer cls_score
I0301 12:09:08.026063 25742 net.cpp:454] cls_score <- fc7_drop7_0_split_0
I0301 12:09:08.026073 25742 net.cpp:411] cls_score -> cls_score
I0301 12:09:08.026279 25742 net.cpp:150] Setting up cls_score
I0301 12:09:08.026288 25742 net.cpp:157] Top shape: 1 2 (2)
I0301 12:09:08.026290 25742 net.cpp:165] Memory required for data: 501106484
I0301 12:09:08.026298 25742 layer_factory.hpp:77] Creating layer bbox_pred
I0301 12:09:08.026305 25742 net.cpp:106] Creating Layer bbox_pred
I0301 12:09:08.026310 25742 net.cpp:454] bbox_pred <- fc7_drop7_0_split_1
I0301 12:09:08.026316 25742 net.cpp:411] bbox_pred -> bbox_pred
I0301 12:09:08.026585 25742 net.cpp:150] Setting up bbox_pred
I0301 12:09:08.026592 25742 net.cpp:157] Top shape: 1 8 (8)
I0301 12:09:08.026595 25742 net.cpp:165] Memory required for data: 501106516
I0301 12:09:08.026602 25742 layer_factory.hpp:77] Creating layer loss_cls
I0301 12:09:08.026618 25742 net.cpp:106] Creating Layer loss_cls
I0301 12:09:08.026623 25742 net.cpp:454] loss_cls <- cls_score
I0301 12:09:08.026629 25742 net.cpp:454] loss_cls <- labels
I0301 12:09:08.026634 25742 net.cpp:411] loss_cls -> loss_cls
I0301 12:09:08.026643 25742 layer_factory.hpp:77] Creating layer loss_cls
I0301 12:09:08.033823 25742 net.cpp:150] Setting up loss_cls
I0301 12:09:08.033846 25742 net.cpp:157] Top shape: (1)
I0301 12:09:08.033849 25742 net.cpp:160] with loss weight 1
I0301 12:09:08.033865 25742 net.cpp:165] Memory required for data: 501106520
I0301 12:09:08.033870 25742 layer_factory.hpp:77] Creating layer loss_bbox
I0301 12:09:08.033885 25742 net.cpp:106] Creating Layer loss_bbox
I0301 12:09:08.033893 25742 net.cpp:454] loss_bbox <- bbox_pred
I0301 12:09:08.033905 25742 net.cpp:454] loss_bbox <- bbox_targets
I0301 12:09:08.033915 25742 net.cpp:454] loss_bbox <- bbox_inside_weights
I0301 12:09:08.033921 25742 net.cpp:454] loss_bbox <- bbox_outside_weights
I0301 12:09:08.033932 25742 net.cpp:411] loss_bbox -> loss_bbox
I0301 12:09:08.034094 25742 net.cpp:150] Setting up loss_bbox
I0301 12:09:08.034101 25742 net.cpp:157] Top shape: (1)
I0301 12:09:08.034104 25742 net.cpp:160] with loss weight 1
I0301 12:09:08.034109 25742 net.cpp:165] Memory required for data: 501106524
I0301 12:09:08.034113 25742 net.cpp:226] loss_bbox needs backward computation.
I0301 12:09:08.034119 25742 net.cpp:226] loss_cls needs backward computation.
I0301 12:09:08.034124 25742 net.cpp:226] bbox_pred needs backward computation.
I0301 12:09:08.034129 25742 net.cpp:226] cls_score needs backward computation.
I0301 12:09:08.034138 25742 net.cpp:226] fc7_drop7_0_split needs backward computation.
I0301 12:09:08.034143 25742 net.cpp:226] drop7 needs backward computation.
I0301 12:09:08.034148 25742 net.cpp:226] relu7 needs backward computation.
I0301 12:09:08.034152 25742 net.cpp:226] fc7 needs backward computation.
I0301 12:09:08.034158 25742 net.cpp:226] drop6 needs backward computation.
I0301 12:09:08.034163 25742 net.cpp:226] relu6 needs backward computation.
I0301 12:09:08.034170 25742 net.cpp:226] fc6 needs backward computation.
I0301 12:09:08.034176 25742 net.cpp:226] roi_pool5 needs backward computation.
I0301 12:09:08.034184 25742 net.cpp:226] roi-data needs backward computation.
I0301 12:09:08.034193 25742 net.cpp:226] proposal needs backward computation.
I0301 12:09:08.034204 25742 net.cpp:226] rpn_cls_prob_reshape needs backward computation.
I0301 12:09:08.034214 25742 net.cpp:226] rpn_cls_prob needs backward computation.
I0301 12:09:08.034220 25742 net.cpp:226] rpn_loss_bbox needs backward computation.
I0301 12:09:08.034229 25742 net.cpp:226] rpn_loss_cls needs backward computation.
I0301 12:09:08.034235 25742 net.cpp:226] rpn-data needs backward computation.
I0301 12:09:08.034245 25742 net.cpp:226] rpn_cls_score_reshape_rpn_cls_score_reshape_0_split needs backward computation.
I0301 12:09:08.034253 25742 net.cpp:226] rpn_cls_score_reshape needs backward computation.
I0301 12:09:08.034260 25742 net.cpp:226] rpn_bbox_pred_rpn_bbox_pred_0_split needs backward computation.
I0301 12:09:08.034268 25742 net.cpp:226] rpn_bbox_pred needs backward computation.
I0301 12:09:08.034277 25742 net.cpp:226] rpn_cls_score_rpn_cls_score_0_split needs backward computation.
I0301 12:09:08.034283 25742 net.cpp:226] rpn_cls_score needs backward computation.
I0301 12:09:08.034291 25742 net.cpp:226] rpn/output_rpn_relu/3x3_0_split needs backward computation.
I0301 12:09:08.034301 25742 net.cpp:226] rpn_relu/3x3 needs backward computation.
I0301 12:09:08.034309 25742 net.cpp:226] rpn_conv/3x3 needs backward computation.
I0301 12:09:08.034317 25742 net.cpp:226] conv5_relu5_0_split needs backward computation.
I0301 12:09:08.034325 25742 net.cpp:226] relu5 needs backward computation.
I0301 12:09:08.034330 25742 net.cpp:226] conv5 needs backward computation.
I0301 12:09:08.034337 25742 net.cpp:226] relu4 needs backward computation.
I0301 12:09:08.034343 25742 net.cpp:226] conv4 needs backward computation.
I0301 12:09:08.034348 25742 net.cpp:226] relu3 needs backward computation.
I0301 12:09:08.034353 25742 net.cpp:226] conv3 needs backward computation.
I0301 12:09:08.034358 25742 net.cpp:226] pool2 needs backward computation.
I0301 12:09:08.034364 25742 net.cpp:226] norm2 needs backward computation.
I0301 12:09:08.034370 25742 net.cpp:226] relu2 needs backward computation.
I0301 12:09:08.034375 25742 net.cpp:226] conv2 needs backward computation.
I0301 12:09:08.034382 25742 net.cpp:228] pool1 does not need backward computation.
I0301 12:09:08.034389 25742 net.cpp:228] norm1 does not need backward computation.
I0301 12:09:08.034394 25742 net.cpp:228] relu1 does not need backward computation.
I0301 12:09:08.034399 25742 net.cpp:228] conv1 does not need backward computation.
I0301 12:09:08.034407 25742 net.cpp:228] gt_boxes_input-data_2_split does not need backward computation.
I0301 12:09:08.034417 25742 net.cpp:228] im_info_input-data_1_split does not need backward computation.
I0301 12:09:08.034425 25742 net.cpp:228] data_input-data_0_split does not need backward computation.
I0301 12:09:08.034431 25742 net.cpp:228] input-data does not need backward computation.
I0301 12:09:08.034436 25742 net.cpp:270] This network produces output loss_bbox
I0301 12:09:08.034442 25742 net.cpp:270] This network produces output loss_cls
I0301 12:09:08.034447 25742 net.cpp:270] This network produces output rpn_cls_loss
I0301 12:09:08.034456 25742 net.cpp:270] This network produces output rpn_loss_bbox
I0301 12:09:08.034523 25742 net.cpp:283] Network initialization done.
I0301 12:09:08.034708 25742 solver.cpp:60] Solver scaffolding done.
Loading pretrained model weights from data/imagenet_models/VGG_CNN_M_1024_Scale.v2.caffemodel
I0301 12:09:21.583925 25742 net.cpp:816] Ignoring source layer pool5
I0301 12:09:21.701347 25742 net.cpp:816] Ignoring source layer fc8
I0301 12:09:21.701382 25742 net.cpp:816] Ignoring source layer prob
Solving...
Traceback (most recent call last):File "./tools/train_net.py", line 112, in <module>max_iters=args.max_iters)File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/fast_rcnn/train.py", line 161, in train_netmodel_paths = sw.train_model(max_iters)File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/fast_rcnn/train.py", line 102, in train_modelself.solver.step(1)File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/rpnS/proposal_target_layer.py", line 66, in forwardrois_per_image, self._num_classes)File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/rpnS/proposal_target_layer.py", line 191, in _sample_rois_get_bbox_regression_labels(bbox_target_data, num_classes)File "/home/lyh/mitosis_DET/DeepMitosis/DeepDetection/tools/../lib/rpnS/proposal_target_layer.py", line 127, in _get_bbox_regression_labelsbbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
TypeError: slice indices must be integers or None or have an __index__ methodhttps://blog.csdn.net/qq_27637315/article/details/78849756
/DeepMitosis/DeepDetection/tools/…/lib/rpnS/proposal_target_layer.py，将
for ind in inds:
cls = clss[ind]
start = 4 * cls
end = start + 4
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
return bbox_targets, bbox_inside_weights
改为：
for ind in inds:
ind = int(ind)
cls = clss[ind]
start = int(4 * cos)
end = int(start + 4)
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
return bbox_targets, bbox_inside_weights
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