encountering segmentation fault while running demo.py about fcis HOT 7 OPEN

I can run demo.py, but the output folder has no outputs, and show_masks function just show me some original demo images, no changes. so have you solved your problem? I also faced your problem before, If i remember correctly, it means lack of fcis_coco-0000.params?

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@encodingwaddles I'm facing the same problem. And @lc8631058 I am quite sure that I get fcis_coco-0000.params downloaded in the correct folder.

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@encodingwaddles I did some debug and found that the segmentation fault happens in the following statement in DataParallelExecutorGroup.py
arg_arr = nd.zeros(arg_shape, context, dtype=arg_type)
Where
arg_shape=(1L, 3L, 600L, 1000L)
But when I check the usage of GPU memory by nvidia-smi, it shows that GPU memory is absolutely sufficient. Does anyone know the possible reason of this?

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@encodingwaddles I use AWS EC2 instance, so I found the answer in #21 , in my environment I can't run the GPU correctly, so I change to cpu according the answer.

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@lc8631058 I tried doing that but I am still getting a segmentation fault. For the AWS EC2 instance, what instance type did you use? Maybe I'll try testing on there instead ...
@mmclkv were you able to get past this error?

Thanks for your help !

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Hi, I am facing same issue as @encodingwaddles. Did you find what was the problem?

python ./fcis/demo.py
('use mxnet at', '/usr/lib/python2.7/site-packages/mxnet-0.11.1-py2.7.egg/mxnet/init.pyc')
{'BINARY_THRESH': 0.4,
'CLASS_AGNOSTIC': True,
'MASK_SIZE': 21,
'MXNET_VERSION': 'mxnet',
'SCALES': [(600, 1000)],
'TEST': {'BATCH_IMAGES': 1,
'CXX_PROPOSAL': False,
'HAS_RPN': True,
'ITER': 2,
'MASK_MERGE_THRESH': 0.5,
'MIN_DROP_SIZE': 2,
'NMS': 0.3,
'PROPOSAL_MIN_SIZE': 2,
'PROPOSAL_NMS_THRESH': 0.7,
'PROPOSAL_POST_NMS_TOP_N': 2000,
'PROPOSAL_PRE_NMS_TOP_N': 20000,
'RPN_MIN_SIZE': 2,
'RPN_NMS_THRESH': 0.7,
'RPN_POST_NMS_TOP_N': 300,
'RPN_PRE_NMS_TOP_N': 6000,
'USE_GPU_MASK_MERGE': True,
'USE_MASK_MERGE': True,
'test_epoch': 8},
'TRAIN': {'ASPECT_GROUPING': True,
'BATCH_IMAGES': 1,
'BATCH_ROIS': -1,
'BATCH_ROIS_OHEM': 128,
'BBOX_MEANS': [0.0, 0.0, 0.0, 0.0],
'BBOX_NORMALIZATION_PRECOMPUTED': True,
'BBOX_REGRESSION_THRESH': 0.5,
'BBOX_STDS': [0.2, 0.2, 0.5, 0.5],
'BBOX_WEIGHTS': array([ 1., 1., 1., 1.]),
'BG_THRESH_HI': 0.5,
'BG_THRESH_LO': 0,
'BINARY_THRESH': 0.4,
'CONVNEW3': True,
'CXX_PROPOSAL': False,
'ENABLE_OHEM': True,
'END2END': True,
'FG_FRACTION': 0.25,
'FG_THRESH': 0.5,
'FLIP': True,
'GAP_SELECT_FROM_ALL': False,
'IGNORE_GAP': False,
'LOSS_WEIGHT': [1.0, 10.0, 1.0],
'RESUME': False,
'RPN_ALLOWED_BORDER': 0,
'RPN_BATCH_SIZE': 256,
'RPN_BBOX_WEIGHTS': [1.0, 1.0, 1.0, 1.0],
'RPN_CLOBBER_POSITIVES': False,
'RPN_FG_FRACTION': 0.5,
'RPN_MIN_SIZE': 2,
'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': 300,
'RPN_PRE_NMS_TOP_N': 6000,
'SHUFFLE': True,
'begin_epoch': 0,
'end_epoch': 8,
'lr': 0.0005,
'lr_step': '5.33',
'model_prefix': 'e2e',
'momentum': 0.9,
'warmup': True,
'warmup_lr': 5e-05,
'warmup_step': 250,
'wd': 0.0005},
'dataset': {'NUM_CLASSES': 81,
'dataset': 'coco',
'dataset_path': './data/coco',
'image_set': 'train2014+valminusminival2014',
'proposal': 'rpn',
'root_path': './data',
'test_image_set': 'test-dev2015'},
'default': {'frequent': 20, 'kvstore': 'device'},
'gpus': '0',
'network': {'ANCHOR_RATIOS': [0.5, 1, 2],
'ANCHOR_SCALES': [4, 8, 16, 32],
'FIXED_PARAMS': ['conv1',
'bn_conv1',
'res2',
'bn2',
'gamma',
'beta'],
'FIXED_PARAMS_SHARED': ['conv1',
'bn_conv1',
'res2',
'bn2',
'res3',
'bn3',
'res4',
'bn4',
'gamma',
'beta'],
'IMAGE_STRIDE': 0,
'NUM_ANCHORS': 12,
'PIXEL_MEANS': array([ 103.06, 115.9 , 123.15]),
'RCNN_FEAT_STRIDE': 16,
'RPN_FEAT_STRIDE': 16,
'pretrained': './model/pretrained_model/resnet_v1_101',
'pretrained_epoch': 0},
'output_path': '../output/fcis',
'symbol': 'resnet_v1_101_fcis'}
Segmentation fault (core dumped)

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@nhanteh I'm guessing it's some dependency conflict or something like that b/c I built it on an aws EC2 machine and I didn't have this error anymore

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