zhanghui-hunan
commented
6 years ago The solution is: enter the shell, first uninstall the graphics card driver, and then download the graphics card driver version corresponding to your own graphics card (such as my 1080Ti corresponding graphics driver 390.77, the official website can be queried) PS: Remember to execute： sudo /etc/init.d/lightdm stop before uninstalling; execute： sudo /etc/init.d/lightdm start after installation (the purpose is to turn off x_driver，otherwise the graphics driver cannot be installed)
use mxnet at /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-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': False, 'USE_MASK_MERGE': False, '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'} Traceback (most recent call last): File "./fcis/demo.py", line 151, in
main()
File "./fcis/demo.py", line 82, in main
arg_params=arg_params, aux_params=aux_params)
File "/home/dl/FCIS/fcis/core/tester.py", line 35, in init
self._mod.bind(provide_data, provide_label, for_training=False)
File "/home/dl/FCIS/fcis/core/module.py", line 845, in bind
for_training, inputs_need_grad, force_rebind=False, shared_module=None)
File "/home/dl/FCIS/fcis/core/module.py", line 402, in bind
state_names=self._state_names)
File "/home/dl/FCIS/fcis/core/DataParallelExecutorGroup.py", line 184, in init
self.bind_exec(data_shapes, label_shapes, shared_group)
File "/home/dl/FCIS/fcis/core/DataParallelExecutorGroup.py", line 284, in bind_exec
shared_group))
File "/home/dl/FCIS/fcis/core/DataParallelExecutorGroup.py", line 598, in _bind_ith_exec
context, self.logger)
File "/home/dl/FCIS/fcis/core/DataParallelExecutorGroup.py", line 576, in _get_or_reshape
arg_arr = nd.zeros(arg_shape, context, dtype=arg_type)
File "/usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/ndarray/utils.py", line 67, in zeros
return _zeros_ndarray(shape, ctx, dtype, kwargs)
File "/usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/ndarray/ndarray.py", line 3750, in zeros
return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, kwargs)
File "", line 34, in _zeros
File "/usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/_ctypes/ndarray.py", line 92, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/base.py", line 252, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [15:20:32] src/engine/threaded_engine.cc:320: Check failed: devicecount > 0 (-1 vs. 0) GPU usage requires at least 1 GPU
Stack trace returned 10 entries: [bt] (0) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5b) [0x7f067ebf7afb] [bt] (1) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28) [0x7f067ebf8668] [bt] (2) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(mxnet::engine::ThreadedEngine::PushAsync(std::function<void (mxnet::RunContext, mxnet::engine::CallbackOnComplete)>, mxnet::Context, std::vector<mxnet::engine::Var, std::allocator<mxnet::engine::Var> > const&, std::vector<mxnet::engine::Var, std::allocator<mxnet::engine::Var> > const&, mxnet::FnProperty, int, char const, bool)+0x4bf) [0x7f0681aee35f] [bt] (3) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(mxnet::engine::ThreadedEngine::PushSync(std::function<void (mxnet::RunContext)>, mxnet::Context, std::vector<mxnet::engine::Var, std::allocator<mxnet::engine::Var> > const&, std::vector<mxnet::engine::Var, std::allocator<mxnet::engine::Var> > const&, mxnet::FnProperty, int, char const)+0x232) [0x7f0681af0702] [bt] (4) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(mxnet::imperative::PushFCompute(std::function<void (nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator > const&, std::vector<mxnet::OpReqType, std::allocator > const&, std::vector<mxnet::TBlob, std::allocator > const&)> const&, nnvm::Op const, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var, std::allocator<mxnet::engine::Var> > const&, std::vector<mxnet::engine::Var, std::allocator<mxnet::engine::Var> > const&, std::vector<mxnet::Resource, std::allocator > const&, std::vector<mxnet::NDArray , std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&, std::vector<unsigned int, std::allocator > const&, std::vector<mxnet::OpReqType, std::allocator > const&)+0x278) [0x7f06815992c8]
[bt] (5) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(mxnet::Imperative::InvokeOp(mxnet::Context const&, nnvm::NodeAttrs const&, std::vector<mxnet::NDArray , std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::OpReqType, std::allocator > const&, mxnet::DispatchMode, mxnet::OpStatePtr)+0x62a) [0x7f068157605a]
[bt] (6) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(mxnet::Imperative::Invoke(mxnet::Context const&, nnvm::NodeAttrs const&, std::vector<mxnet::NDArray , std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&)+0x63d) [0x7f0681576ffd]
[bt] (7) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(MXImperativeInvokeImpl(void, int, void, int, void, int, char const, char const)+0xe90) [0x7f0681b37040]
[bt] (8) /usr/local/lib/python2.7/dist-packages/mxnet-1.3.0-py2.7.egg/mxnet/libmxnet.so(MXImperativeInvokeEx+0x40b) [0x7f0681b392bb]
[bt] (9) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7f0691ba2e40]
someone could help me ?