Comments (10)
The problem is resolved.
In the fcis_coco_demo.yaml file both the USE_MASK_MERGE and USE_GPU_MASK_MERGE should be set as false then it works.
# ITER 2 & mask merge
ITER: 2
MIN_DROP_SIZE: 2
USE_MASK_MERGE: false
USE_GPU_MASK_MERGE: false
from fcis.
now I run demo.py
correctly in Jupyter notebook, but the show_masks
function just output the original demo images without any changes, I wonder why?
from fcis.
just found the answer in #21
from fcis.
I get this problem
'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'}
[15:55:18] /home/sensetime/mxnet/dmlc-core/include/dmlc/./logging.h:300: [15:55:18] src/c_api/c_api_ndarray.cc:390: Operator _zeros cannot be run; requires at least one of FCompute<xpu>, NDArrayFunction, FCreateOperator be registered
Stack trace returned 10 entries:
[bt] (0) /usr/lib/python2.7/site-packages/mxnet-0.9.5-py2.7.egg/mxnet/libmxnet.so(_ZN4dmlc15LogMessageFatalD1Ev+0x29) [0x7f86b7c0e129]
[bt] (1) /usr/lib/python2.7/site-packages/mxnet-0.9.5-py2.7.egg/mxnet/libmxnet.so(MXImperativeInvoke+0x640) [0x7f86b885f8a0]
[bt] (2) /lib64/libffi.so.6(ffi_call_unix64+0x4c) [0x7f86bfec0dcc]
[bt] (3) /lib64/libffi.so.6(ffi_call+0x1f5) [0x7f86bfec06f5]
[bt] (4) /usr/lib64/python2.7/lib-dynload/_ctypes.so(_ctypes_callproc+0x30b) [0x7f8694d33c8b]
[bt] (5) /usr/lib64/python2.7/lib-dynload/_ctypes.so(+0xaa85) [0x7f8694d2da85]
[bt] (6) /lib64/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7f86d1cf78e3]
[bt] (7) /lib64/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x2336) [0x7f86d1d8c036]
[bt] (8) /lib64/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x7ed) [0x7f86d1d92e3d]
[bt] (9) /lib64/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x663c) [0x7f86d1d9033c]
Traceback (most recent call last):
File "./fcis/demo.py", line 153, in <module>
main()
File "./fcis/demo.py", line 84, in main
arg_params=arg_params, aux_params=aux_params)
File "/home/sensetime/FCIS/fcis/core/tester.py", line 30, in __init__
self._mod.bind(provide_data, provide_label, for_training=False)
File "/home/sensetime/FCIS/fcis/core/module.py", line 840, in bind
for_training, inputs_need_grad, force_rebind=False, shared_module=None)
File "/home/sensetime/FCIS/fcis/core/module.py", line 397, in bind
state_names=self._state_names)
File "/home/sensetime/FCIS/fcis/core/DataParallelExecutorGroup.py", line 178, in __init__
self.bind_exec(data_shapes, label_shapes, shared_group)
File "/home/sensetime/FCIS/fcis/core/DataParallelExecutorGroup.py", line 278, in bind_exec
shared_group))
File "/home/sensetime/FCIS/fcis/core/DataParallelExecutorGroup.py", line 592, in _bind_ith_exec
context, self.logger)
File "/home/sensetime/FCIS/fcis/core/DataParallelExecutorGroup.py", line 570, in _get_or_reshape
arg_arr = nd.zeros(arg_shape, context, dtype=arg_type)
File "/usr/lib/python2.7/site-packages/mxnet-0.9.5-py2.7.egg/mxnet/ndarray.py", line 946, in zeros
return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype)
File "/usr/lib/python2.7/site-packages/mxnet-0.9.5-py2.7.egg/mxnet/_ctypes/ndarray.py", line 164, in generic_ndarray_function
c_array(ctypes.c_char_p, [c_str(val) for val in vals])))
File "/usr/lib/python2.7/site-packages/mxnet-0.9.5-py2.7.egg/mxnet/base.py", line 78, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [15:55:18] src/c_api/c_api_ndarray.cc:390: Operator _zeros cannot be run; requires at least one of FCompute<xpu>, NDArrayFunction, FCreateOperator be registered
Stack trace returned 10 entries:
[bt] (0) /usr/lib/python2.7/site-packages/mxnet-0.9.5-py2.7.egg/mxnet/libmxnet.so(_ZN4dmlc15LogMessageFatalD1Ev+0x29) [0x7f86b7c0e129]
[bt] (1) /usr/lib/python2.7/site-packages/mxnet-0.9.5-py2.7.egg/mxnet/libmxnet.so(MXImperativeInvoke+0x640) [0x7f86b885f8a0]
[bt] (2) /lib64/libffi.so.6(ffi_call_unix64+0x4c) [0x7f86bfec0dcc]
[bt] (3) /lib64/libffi.so.6(ffi_call+0x1f5) [0x7f86bfec06f5]
[bt] (4) /usr/lib64/python2.7/lib-dynload/_ctypes.so(_ctypes_callproc+0x30b) [0x7f8694d33c8b]
[bt] (5) /usr/lib64/python2.7/lib-dynload/_ctypes.so(+0xaa85) [0x7f8694d2da85]
[bt] (6) /lib64/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7f86d1cf78e3]
[bt] (7) /lib64/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x2336) [0x7f86d1d8c036]
[bt] (8) /lib64/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x7ed) [0x7f86d1d92e3d]
[bt] (9) /lib64/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x663c) [0x7f86d1d9033c]
from fcis.
Hi, @fangxu622 did you do the following steps before building mxnet?
git checkout 62ecb60
git submodule update
from fcis.
use mxnet at /home/jimmy/mxnet/python/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': False,
'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'}
[12:00:36] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:00:36] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:00:36] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:00:37] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:00:37] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:00:37] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
(426, 640)
invalid device function
invalid device function
testing COCO_test2015_000000000275.jpg 2.1574s
[12:00:54] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:00:54] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:00:54] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
(427, 640)
invalid device function
invalid device function
testing COCO_test2015_000000001412.jpg 2.2110s
(427, 640)
invalid device function
invalid device function
testing COCO_test2015_000000073428.jpg 2.1906s
[12:01:05] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:01:05] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
[12:01:05] src/operator/convolution.cu:87: This convolution is not supported by cudnn, MXNET convolution is applied.
(428, 640)
invalid device function
invalid device function
testing COCO_test2015_000000393281.jpg 2.1758s
done
similar output on the HP laptop. The mask is not correctly shown. On aws p2, no issues.
from fcis.
The key issue here might be the "invalid device function" . I changed the mask voting to CPU version. It doesn't work either.
from fcis.
@liketheflower Thank you for the comments. They help me a lot to resolve my problems. However, setting both flags as false is not the optimal solution. Rather, we should set either one as true according to the cpu or gpu mode we use. In this way, we can obtain more accurate segmentation results.
from fcis.
@tfzhou I think you are right. I found the result when using the default mode is more accurate.
from fcis.
@liyi14
I got fatal when I run this : git submodule update
fatal: reference is not a tree: 89de7ab20167909bc2c4f8acd397671c47cf3c0d
Submodule path 'dmlc-core': checked out 'b5bec5481df86e8e6728d8bd80a61d87ef3b2cd5'
Submodule path 'mshadow': checked out '23210f3939428e42bc34553469ed9ce8c63001ed'
Submodule path 'nnvm': checked out 'ddf3c17e3455db9cd10f5b18bc9753a146971819'
Unable to checkout '89de7ab20167909bc2c4f8acd397671c47cf3c0d' in submodule path 'cub'
from fcis.
Related Issues (20)
- TypeError: _update_params_on_kvstore() takes exactly 4 arguments (3 given) HOT 1
- there is an error when trained on ResNet-50 model HOT 1
- Index out of bounds error in line 143 of proposal_annotator.py HOT 1
- Some problems encountered when using deconvolution
- mxnet.base.MXNetError: [15:20:32] src/engine/threaded_engine.cc:320: Check failed: device_count_ > 0 (-1 vs. 0) GPU usage requires at least 1 GPU HOT 1
- The result finetune with cityscapes is very poor
- Network structure
- When I train my own coco-like datasets,I successfully train and test the model,but the result of all AP is 0. HOT 5
- If my data is small,such as 6000 images,is it difficult to train the model? HOT 3
- my own data set ,test map is 0
- 有哪位遇到过训练成功,测试map=0的情况吗 HOT 3
- mxnet.base.MXNetError: [16:31:18] src/operator/nn/./cudnn/cudnn_convolution-inl.h:449: Check failed: e == CUDNN_STATUS_SUCCESS (3 vs. 0) cuDNN: CUDNN_STATUS_BAD_PARAM HOT 1
- pip install -r requirements.txt HOT 3
- unable to install ./init.sh error HOT 4
- Problems when launching demo.py
- Is it possible to run FCIS on AMD?
- Trouble building on Windows 10 (TypeError: expected str, bytes or os.PathLike object, not NoneType) HOT 2
- In-depth guide to getting FCIS working on Windows 10 HOT 3
- [HELP] problem with cpu_nms HOT 1
- Result file for COCO test-dev instance segmentation
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from fcis.