ruotianluo/pytorch-faster-rcnn

TypeError: Cannot handle this data type Command exited with non-zero status 1

zhongnanning opened this issue · 6 comments

zhongnanning commented
TypeError: Cannot handle this data type Command exited with non-zero status 1
zhongnanning commented
  • set -e
  • export PYTHONUNBUFFERED=True
  • PYTHONUNBUFFERED=True
  • GPU_ID=0
  • DATASET=pascal_voc
  • NET=vgg16
  • array=($@)
  • len=3
  • EXTRA_ARGS=
  • EXTRA_ARGS_SLUG=
  • case ${DATASET} in
  • TRAIN_IMDB=voc_2007_trainval
  • TEST_IMDB=voc_2007_test
  • STEPSIZE='[50000]'
  • ITERS=70000
  • ANCHORS='[8,16,32]'
  • RATIOS='[0.5,1,2]'
    ++ date +%Y-%m-%d_%H-%M-%S
  • LOG=experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2018-11-08_21-33-49
  • exec
    ++ tee -a experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2018-11-08_21-33-49
  • echo Logging output to experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2018-11-08_21-33-49
    Logging output to experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2018-11-08_21-33-49
  • set +x
  • '[' '!' -f output/vgg16/voc_2007_trainval/default/vgg16_faster_rcnn_iter_70000.pth.index ']'
  • [[ ! -z '' ]]
  • CUDA_VISIBLE_DEVICES=0
  • time python ./tools/trainval_net.py --weight data/imagenet_weights/vgg16.pth --imdb voc_2007_trainval --imdbval voc_2007_test --iters 70000 --cfg experiments/cfgs/vgg16.yml --net vgg16 --set ANCHOR_SCALES '[8,16,32]' ANCHOR_RATIOS '[0.5,1,2]' TRAIN.STEPSIZE '[50000]'
    Called with args:
    Namespace(cfg_file='experiments/cfgs/vgg16.yml', imdb_name='voc_2007_trainval', imdbval_name='voc_2007_test', max_iters=70000, net='vgg16', set_cfgs=['ANCHOR_SCALES', '[8,16,32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'TRAIN.STEPSIZE', '[50000]'], tag=None, weight='data/imagenet_weights/vgg16.pth')
    Using config:
    {'ANCHOR_RATIOS': [0.5, 1, 2],
    'ANCHOR_SCALES': [8, 16, 32],
    'DATA_DIR': '/home/zhong/pytorch-faster-rcnn/data',
    'EXP_DIR': 'vgg16',
    'MATLAB': 'matlab',
    'MOBILENET': {'DEPTH_MULTIPLIER': 1.0,
    'FIXED_LAYERS': 5,
    'REGU_DEPTH': False,
    'WEIGHT_DECAY': 4e-05},
    'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),
    'POOLING_MODE': 'crop',
    'POOLING_SIZE': 7,
    'RESNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False},
    'RNG_SEED': 3,
    'ROOT_DIR': '/home/zhong/pytorch-faster-rcnn',
    'RPN_CHANNELS': 512,
    'TEST': {'BBOX_REG': True,
    'HAS_RPN': True,
    'MAX_SIZE': 1000,
    'MODE': 'nms',
    'NMS': 0.3,
    'PROPOSAL_METHOD': 'gt',
    'RPN_NMS_THRESH': 0.7,
    'RPN_POST_NMS_TOP_N': 300,
    'RPN_PRE_NMS_TOP_N': 6000,
    'RPN_TOP_N': 5000,
    'SCALES': [600],
    'SVM': False},
    'TRAIN': {'ASPECT_GROUPING': False,
    'BATCH_SIZE': 256,
    '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,
    'BIAS_DECAY': False,
    'DISPLAY': 20,
    'DOUBLE_BIAS': True,
    'FG_FRACTION': 0.25,
    'FG_THRESH': 0.5,
    'GAMMA': 0.1,
    'HAS_RPN': True,
    'IMS_PER_BATCH': 1,
    'LEARNING_RATE': 0.001,
    'MAX_SIZE': 1000,
    'MOMENTUM': 0.9,
    '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_NEGATIVE_OVERLAP': 0.3,
    'RPN_NMS_THRESH': 0.7,
    'RPN_POSITIVE_OVERLAP': 0.7,
    'RPN_POSITIVE_WEIGHT': -1.0,
    'RPN_POST_NMS_TOP_N': 2000,
    'RPN_PRE_NMS_TOP_N': 12000,
    'SCALES': [600],
    'SNAPSHOT_ITERS': 5000,
    'SNAPSHOT_KEPT': 3,
    'SNAPSHOT_PREFIX': 'vgg16_faster_rcnn',
    'STEPSIZE': [50000],
    'SUMMARY_INTERVAL': 180,
    'TRUNCATED': False,
    'USE_ALL_GT': True,
    'USE_FLIPPED': True,
    'USE_GT': False,
    'WEIGHT_DECAY': 0.0001},
    'USE_GPU_NMS': True}
    Loaded dataset voc_2007_trainval for training
    Set proposal method: gt
    Appending horizontally-flipped training examples...
    voc_2007_trainval gt roidb loaded from /home/zhong/pytorch-faster-rcnn/data/cache/voc_2007_trainval_gt_roidb.pkl
    done
    Preparing training data...
    done
    42 roidb entries
    Output will be saved to /home/zhong/pytorch-faster-rcnn/output/vgg16/voc_2007_trainval/default
    TensorFlow summaries will be saved to /home/zhong/pytorch-faster-rcnn/tensorboard/vgg16/voc_2007_trainval/default
    Loaded dataset voc_2007_test for training
    Set proposal method: gt
    Preparing training data...
    voc_2007_test gt roidb loaded from /home/zhong/pytorch-faster-rcnn/data/cache/voc_2007_test_gt_roidb.pkl
    done
    10 validation roidb entries
    Filtered 0 roidb entries: 42 -> 42
    Filtered 0 roidb entries: 10 -> 10
    Solving...
    Loading initial model weights from data/imagenet_weights/vgg16.pth
    Loaded.
    Traceback (most recent call last):
    File "/home/zhong/anaconda3/lib/python3.6/site-packages/PIL/Image.py", line 2428, in fromarray
    mode, rawmode = _fromarray_typemap[typekey]
    KeyError: ((1, 1, 333), '|u1')

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "./tools/trainval_net.py", line 138, in
max_iters=args.max_iters)
File "/home/zhong/pytorch-faster-rcnn/tools/../lib/model/train_val.py", line 348, in train_net
sw.train_model(max_iters)
File "/home/zhong/pytorch-faster-rcnn/tools/../lib/model/train_val.py", line 255, in train_model
self.net.train_step_with_summary(blobs, self.optimizer)
File "/home/zhong/pytorch-faster-rcnn/tools/../lib/nets/network.py", line 470, in train_step_with_summary
summary = self._run_summary_op()
File "/home/zhong/pytorch-faster-rcnn/tools/../lib/nets/network.py", line 326, in _run_summary_op
summaries.append(self._add_gt_image_summary())
File "/home/zhong/pytorch-faster-rcnn/tools/../lib/nets/network.py", line 65, in _add_gt_image_summary
return tb.summary.image('GROUND_TRUTH', image[0].astype('float32')/255.0)
File "/home/zhong/anaconda3/lib/python3.6/site-packages/tensorboardX/summary.py", line 205, in image
image = make_image(tensor, rescale=rescale)
File "/home/zhong/anaconda3/lib/python3.6/site-packages/tensorboardX/summary.py", line 243, in make_image
image = Image.fromarray(tensor)
File "/home/zhong/anaconda3/lib/python3.6/site-packages/PIL/Image.py", line 2431, in fromarray
raise TypeError("Cannot handle this data type")
TypeError: Cannot handle this data type
Command exited with non-zero status 1
5.83user 1.54system 0:06.26elapsed 117%CPU (0avgtext+0avgdata 3345880maxresident)k
0inputs+16outputs (0major+839969minor)pagefaults 0swaps

zhongnanning commented

I made my own data set with my heart. The data set refers to the format of VOC2007, but when I train, I get error...

zhongnanning commented

xiongdi ni shi zhong guo ren ma?

daibin88 commented

I have encountered a similar problem,is the problem solved?

daibin88 commented

./experiments/scripts/train_faster_rcnn.sh 0 pascal_voc vgg16

  • set -e
  • export PYTHONUNBUFFERED=True
  • PYTHONUNBUFFERED=True
  • GPU_ID=0
  • DATASET=pascal_voc
  • NET=vgg16
  • array=($@)
  • len=3
  • EXTRA_ARGS=
  • EXTRA_ARGS_SLUG=
  • case ${DATASET} in
  • TRAIN_IMDB=voc_2007_trainval
  • TEST_IMDB=voc_2007_test
  • STEPSIZE='[50000]'
  • ITERS=70000
  • ANCHORS='[8,16,32]'
  • RATIOS='[0.5,1,2]'
    ++ date +%Y-%m-%d_%H-%M-%S
  • LOG=experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2018-12-25_16-49-42
  • exec
    ++ tee -a experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2018-12-25_16-49-42
  • echo Logging output to experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2018-12-25_16-49-42
    Logging output to experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2018-12-25_16-49-42
  • set +x
  • '[' '!' -f output/vgg16/voc_2007_trainval/default/vgg16_faster_rcnn_iter_70000.pth.index ']'
  • [[ ! -z '' ]]
  • CUDA_VISIBLE_DEVICES=0
  • time python ./tools/trainval_net.py --weight data/imagenet_weights/vgg16.pth --imdb voc_2007_trainval --imdbval voc_2007_test --iters 70000 --cfg experiments/cfgs/vgg16.yml --net vgg16 --set ANCHOR_SCALES '[8,16,32]' ANCHOR_RATIOS '[0.5,1,2]' TRAIN.STEPSIZE '[50000]'
    Called with args:
    Namespace(cfg_file='experiments/cfgs/vgg16.yml', imdb_name='voc_2007_trainval', imdbval_name='voc_2007_test', max_iters=70000, net='vgg16', set_cfgs=['ANCHOR_SCALES', '[8,16,32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'TRAIN.STEPSIZE', '[50000]'], tag=None, weight='data/imagenet_weights/vgg16.pth')
    Using config:
    {'ANCHOR_RATIOS': [0.5, 1, 2],
    'ANCHOR_SCALES': [8, 16, 32],
    'DATA_DIR': '/home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/data',
    'EXP_DIR': 'vgg16',
    'MATLAB': 'matlab',
    'MOBILENET': {'DEPTH_MULTIPLIER': 1.0,
    'FIXED_LAYERS': 5,
    'REGU_DEPTH': False,
    'WEIGHT_DECAY': 4e-05},
    'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),
    'POOLING_MODE': 'crop',
    'POOLING_SIZE': 7,
    'RESNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False},
    'RNG_SEED': 3,
    'ROOT_DIR': '/home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn',
    'RPN_CHANNELS': 512,
    'TEST': {'BBOX_REG': True,
    'HAS_RPN': True,
    'MAX_SIZE': 1000,
    'MODE': 'nms',
    'NMS': 0.3,
    'PROPOSAL_METHOD': 'gt',
    'RPN_NMS_THRESH': 0.7,
    'RPN_POST_NMS_TOP_N': 300,
    'RPN_PRE_NMS_TOP_N': 6000,
    'RPN_TOP_N': 5000,
    'SCALES': [600],
    'SVM': False},
    'TRAIN': {'ASPECT_GROUPING': False,
    'BATCH_SIZE': 256,
    '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,
    'BIAS_DECAY': False,
    'DISPLAY': 20,
    'DOUBLE_BIAS': True,
    'FG_FRACTION': 0.25,
    'FG_THRESH': 0.5,
    'GAMMA': 0.1,
    'HAS_RPN': True,
    'IMS_PER_BATCH': 1,
    'LEARNING_RATE': 0.001,
    'MAX_SIZE': 1000,
    'MOMENTUM': 0.9,
    '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_NEGATIVE_OVERLAP': 0.3,
    'RPN_NMS_THRESH': 0.7,
    'RPN_POSITIVE_OVERLAP': 0.7,
    'RPN_POSITIVE_WEIGHT': -1.0,
    'RPN_POST_NMS_TOP_N': 2000,
    'RPN_PRE_NMS_TOP_N': 12000,
    'SCALES': [600],
    'SNAPSHOT_ITERS': 5000,
    'SNAPSHOT_KEPT': 3,
    'SNAPSHOT_PREFIX': 'vgg16_faster_rcnn',
    'STEPSIZE': [50000],
    'SUMMARY_INTERVAL': 180,
    'TRUNCATED': False,
    'USE_ALL_GT': True,
    'USE_FLIPPED': True,
    'USE_GT': False,
    'WEIGHT_DECAY': 0.0001},
    'USE_GPU_NMS': True}
    Loaded dataset voc_2007_trainval for training
    Set proposal method: gt
    Appending horizontally-flipped training examples...
    voc_2007_trainval gt roidb loaded from /home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/data/cache/voc_2007_trainval_gt_roidb.pkl
    done
    Preparing training data...
    done
    10022 roidb entries
    Output will be saved to /home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/output/vgg16/voc_2007_trainval/default
    TensorFlow summaries will be saved to /home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/tensorboard/vgg16/voc_2007_trainval/default
    Loaded dataset voc_2007_test for training
    Set proposal method: gt
    Preparing training data...
    voc_2007_test gt roidb loaded from /home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/data/cache/voc_2007_test_gt_roidb.pkl
    done
    4952 validation roidb entries
    Filtered 0 roidb entries: 10022 -> 10022
    Filtered 0 roidb entries: 4952 -> 4952
    Solving...
    Loading initial model weights from data/imagenet_weights/vgg16.pth
    Loaded.
    Traceback (most recent call last):
    File "/home/dane/anaconda3/lib/python3.6/site-packages/PIL/Image.py", line 2490, in fromarray
    mode, rawmode = _fromarray_typemap[typekey]
    KeyError: ((1, 1, 375), '|u1')

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "./tools/trainval_net.py", line 138, in
max_iters=args.max_iters)
File "/home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/tools/../lib/model/train_val.py", line 348, in train_net
sw.train_model(max_iters)
File "/home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/tools/../lib/model/train_val.py", line 255, in train_model
self.net.train_step_with_summary(blobs, self.optimizer)
File "/home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/tools/../lib/nets/network.py", line 470, in train_step_with_summary
summary = self._run_summary_op()
File "/home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/tools/../lib/nets/network.py", line 326, in _run_summary_op
summaries.append(self._add_gt_image_summary())
File "/home/dane/Dane/Detection/fasterrcnn/pytorch-faster-rcnn/tools/../lib/nets/network.py", line 65, in _add_gt_image_summary
return tb.summary.image('GROUND_TRUTH', image[0].astype('float32')/255.0)
File "/home/dane/anaconda3/lib/python3.6/site-packages/tensorboardX/summary.py", line 213, in image
image = make_image(tensor, rescale=rescale)
File "/home/dane/anaconda3/lib/python3.6/site-packages/tensorboardX/summary.py", line 252, in make_image
image = Image.fromarray(tensor)
File "/home/dane/anaconda3/lib/python3.6/site-packages/PIL/Image.py", line 2492, in fromarray
raise TypeError("Cannot handle this data type")
TypeError: Cannot handle this data type
Command exited with non-zero status 1
7.61user 1.12system 0:06.18elapsed 141%CPU (0avgtext+0avgdata 2966816maxresident)k
0inputs+16outputs (0major+813099minor)pagefaults 0swaps

daibin88 commented

OK , the problem is solved. pip install tensorboard-pytorch