kmyi
commented
7 years ago Hi,
It's due to compilation and library problems in your environment. I cannot help you in this case. The error message shows that you have libstdc issues.
Cheers, Kwang
-
-
- 03:58 Hoototo notifications@github.com 작성:
-
Hello, there are some problems when I run ./run.sh hoo104@amax-1080:~/LIFT$ ./run.sh Traceback (most recent call last): File "compute_detector.py", line 47, in from Utils.sift_tools import recomputeOrientation File "/home/hoo104/LIFT/python-code/Utils/sift_tools.py", line 48, in libSIFT = cdll.LoadLibrary('../c-code/libSIFT.so') File "/home/hoo104/anaconda3/lib/python3.5/ctypes/init.py", line 425, in LoadLibrary return self._dlltype(name) File "/home/hoo104/anaconda3/lib/python3.5/ctypes/init.py", line 347, in init self._handle = _dlopen(self._name, mode) OSError: /home/hoo104/anaconda3/lib/python3.5/site-packages/../../libstdc++.so.6: version `CXXABI_1.3.8' not found (required by ../c-code/libSIFT.so) Parameters
--------------------------------------------------------------------------------------
Be careful as we do not use sophisticated parsing
The parser will read until the semicolon
types are defined as below
ss: multiple strings separated with commas
s: string
b: boolean
f: float
d: int
--------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------
Dataset parameters
ss: dataset.trainSetList = ECCV/piccadilly/; # All the first images of oxford dataset is used for training s: dataset.dataType = ECCV; # the dataset type
--------------------------------------------------------------------------------------
Model parameters
s: model.modelType = Combined; # the network type b: model.bNormalizeInput = 1; # Normalize input to have zero mean and 1 std f: model.fScaleList = np.array([1.0]); # list of scale spaces (small means larger scale)
GHH related
f: model.max_strength = -1; # GHH softmax strength (-1 for hard)
Keypoints
s: model.sDetector = tilde; # network architecture for kp d: model.nFilterSize = 25; # Let's keep the number odd d: model.nPatchSizeKp = 48; # patch size for kp including moving
regions we use something smaller
s: model.sKpNonlinearity = None; # use nonlinearity at end f: model.fScaleList = np.array([1.0]); # list of scale spaces (small means larger scale) f: model.bias_rnd = 0.0; # random noise added to bias f: model.epsilon = 1e-8; # epsilon for tilde
Orientation
s: model.sOrientation = cvpr16; # network architecture for orientation
Descriptor
s: model.sDescriptor = descriptor_imported; # network architecture for desc d: model.nDescInputSize = 64; # Input size to be fed to the descriptor
s: model.descriptor_export_folder = /cvlabdata1/home/trulls-data/kpdesc/torch/export/;
L-> skipped s: model.descriptor_model = new-CNN3-picc-iter-56k.h5; # network configuration s: model.descriptor_input = desc-input; # Descriptor input
--------------------------------------------------------------------------------------
Parameters for patch extraction
automatically determined
f: patch.fMaxScale = np.max(self.model.fScaleList); # asserts make sure this is stored properly f: patch.fRatioScale = (self.model.nPatchSizeKp / 2.0) / 2.0; # to not resize when scale is 2 d: patch.nPatchSize = np.round(self.model.nDescInputSize * self.patch.fRatioScale / 6.0); # the large patch size for data. the desc will look at ratio scale of 6.0
--------------------------------------------------------------------------------------
Validation and test time parameters
d: validation.batch_size = 100; # batch size of the implementation
d: validation.nScaleInterval = 4;
L-> skipped
d: validation.nNMSInterval = 2; # number of intervals we look for
L-> skipped
NMS (3 would mean it goes up one
octave in case of
nScaleInterval=2)
---------------------------------------------------------------------------------
Test Data Module
Traceback (most recent call last): File "compute_orientation.py", line 107, in test_data_in = data_module.data_obj(param, image_file_name, kp_file_name) File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 64, in init self.load_data(param, image_file_name, kp_file_name) File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 82, in load_data pathconf, param, image_file_name, kp_file_name) File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 129, in load_data_for_set kp = np.asarray(loadKpListFromTxt(kp_file_name)) File "/home/hoo104/LIFT/python-code/Utils/kp_tools.py", line 196, in loadKpListFromTxt kp_file = open(kp_file_name, 'rb') FileNotFoundError: [Errno 2] No such file or directory: '/home/hoo104/LIFT/results/img1_kp.txt' Parameters
--------------------------------------------------------------------------------------
Be careful as we do not use sophisticated parsing
The parser will read until the semicolon
types are defined as below
ss: multiple strings separated with commas
s: string
b: boolean
f: float
d: int
--------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------
Dataset parameters
ss: dataset.trainSetList = ECCV/piccadilly/; # All the first images of oxford dataset is used for training s: dataset.dataType = ECCV; # the dataset type
--------------------------------------------------------------------------------------
Model parameters
s: model.modelType = Combined; # the network type b: model.bNormalizeInput = 1; # Normalize input to have zero mean and 1 std f: model.fScaleList = np.array([1.0]); # list of scale spaces (small means larger scale)
GHH related
f: model.max_strength = -1; # GHH softmax strength (-1 for hard)
Keypoints
s: model.sDetector = tilde; # network architecture for kp d: model.nFilterSize = 25; # Let's keep the number odd d: model.nPatchSizeKp = 48; # patch size for kp including moving
regions we use something smaller
s: model.sKpNonlinearity = None; # use nonlinearity at end f: model.fScaleList = np.array([1.0]); # list of scale spaces (small means larger scale) f: model.bias_rnd = 0.0; # random noise added to bias f: model.epsilon = 1e-8; # epsilon for tilde
Orientation
s: model.sOrientation = cvpr16; # network architecture for orientation
Descriptor
s: model.sDescriptor = descriptor_imported; # network architecture for desc d: model.nDescInputSize = 64; # Input size to be fed to the descriptor
s: model.descriptor_export_folder = /cvlabdata1/home/trulls-data/kpdesc/torch/export/;
L-> skipped s: model.descriptor_model = new-CNN3-picc-iter-56k.h5; # network configuration s: model.descriptor_input = desc-input; # Descriptor input
--------------------------------------------------------------------------------------
Parameters for patch extraction
automatically determined
f: patch.fMaxScale = np.max(self.model.fScaleList); # asserts make sure this is stored properly f: patch.fRatioScale = (self.model.nPatchSizeKp / 2.0) / 2.0; # to not resize when scale is 2 d: patch.nPatchSize = np.round(self.model.nDescInputSize * self.patch.fRatioScale / 6.0); # the large patch size for data. the desc will look at ratio scale of 6.0
--------------------------------------------------------------------------------------
Validation and test time parameters
d: validation.batch_size = 100; # batch size of the implementation
d: validation.nScaleInterval = 4;
L-> skipped
d: validation.nNMSInterval = 2; # number of intervals we look for
L-> skipped
NMS (3 would mean it goes up one
octave in case of
nScaleInterval=2)
---------------------------------------------------------------------------------
Test Data Module
Traceback (most recent call last): File "compute_descriptor.py", line 111, in test_data_in = data_module.data_obj(param, image_file_name, kp_file_name) File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 64, in init self.load_data(param, image_file_name, kp_file_name) File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 82, in load_data pathconf, param, image_file_name, kp_file_name) File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 135, in load_data_for_set angle = np.pi / 180.0 * kp[:, IDX_ANGLE] # store angle in radians IndexError: too many indices for array
— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.
hanghoo
YuYuYan
NEU-Gou
zhuoligetu123
Hello There are some problems when I run ./run.sh. And I can't find the way to solve it. Traceback (most recent call last): File "compute_detector.py", line 47, in
from Utils.sift_tools import recomputeOrientation
File "/home/hoo104/LIFT/python-code/Utils/sift_tools.py", line 48, in
libSIFT = cdll.LoadLibrary('../c-code/libSIFT.so')
File "/home/hoo104/anaconda3/lib/python3.5/ctypes/init.py", line 425, in LoadLibrary
return self._dlltype(name)
File "/home/hoo104/anaconda3/lib/python3.5/ctypes/init.py", line 347, in init
self._handle = _dlopen(self._name, mode)
OSError: ../c-code/libSIFT.so: undefined symbol: _ZN2cv3hal9fastAtan2EPKfS2_Pfib
Parameters
--------------------------------------------------------------------------------------
Be careful as we do not use sophisticated parsing
The parser will read until the semicolon
types are defined as below
ss: multiple strings separated with commas
s: string
b: boolean
f: float
d: int
--------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------
Dataset parameters
ss: dataset.trainSetList = ECCV/piccadilly/; # All the first images of oxford dataset is used for training s: dataset.dataType = ECCV; # the dataset type
--------------------------------------------------------------------------------------
Model parameters
s: model.modelType = Combined; # the network type b: model.bNormalizeInput = 1; # Normalize input to have zero mean and 1 std f: model.fScaleList = np.array([1.0]); # list of scale spaces (small means larger scale)
GHH related
f: model.max_strength = -1; # GHH softmax strength (-1 for hard)
Keypoints
s: model.sDetector = tilde; # network architecture for kp d: model.nFilterSize = 25; # Let's keep the number odd d: model.nPatchSizeKp = 48; # patch size for kp including moving
regions we use something smaller
s: model.sKpNonlinearity = None; # use nonlinearity at end f: model.fScaleList = np.array([1.0]); # list of scale spaces (small means larger scale) f: model.bias_rnd = 0.0; # random noise added to bias f: model.epsilon = 1e-8; # epsilon for tilde
Orientation
s: model.sOrientation = cvpr16; # network architecture for orientation
Descriptor
s: model.sDescriptor = descriptor_imported; # network architecture for desc d: model.nDescInputSize = 64; # Input size to be fed to the descriptor
s: model.descriptor_export_folder = /cvlabdata1/home/trulls-data/kpdesc/torch/export/;
L-> skipped s: model.descriptor_model = new-CNN3-picc-iter-56k.h5; # network configuration s: model.descriptor_input = desc-input; # Descriptor input
--------------------------------------------------------------------------------------
Parameters for patch extraction
automatically determined
f: patch.fMaxScale = np.max(self.model.fScaleList); # asserts make sure this is stored properly f: patch.fRatioScale = (self.model.nPatchSizeKp / 2.0) / 2.0; # to not resize when scale is 2 d: patch.nPatchSize = np.round(self.model.nDescInputSize * self.patch.fRatioScale / 6.0); # the large patch size for data. the desc will look at ratio scale of 6.0
--------------------------------------------------------------------------------------
Validation and test time parameters
d: validation.batch_size = 100; # batch size of the implementation
d: validation.nScaleInterval = 4;
L-> skipped
d: validation.nNMSInterval = 2; # number of intervals we look for
L-> skipped
NMS (3 would mean it goes up one
octave in case of
nScaleInterval=2)
---------------------------------------------------------------------------------
Test Data Module
Traceback (most recent call last): File "compute_orientation.py", line 107, in
test_data_in = data_module.data_obj(param, image_file_name, kp_file_name)
File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 64, in init
self.load_data(param, image_file_name, kp_file_name)
File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 82, in load_data
pathconf, param, image_file_name, kp_file_name)
File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 129, in load_data_for_set
kp = np.asarray(loadKpListFromTxt(kp_file_name))
File "/home/hoo104/LIFT/python-code/Utils/kp_tools.py", line 196, in loadKpListFromTxt
kp_file = open(kp_file_name, 'rb')
FileNotFoundError: [Errno 2] No such file or directory: '/home/hoo104/LIFT/results/img1_kp.txt'
Parameters
--------------------------------------------------------------------------------------
Be careful as we do not use sophisticated parsing
The parser will read until the semicolon
types are defined as below
ss: multiple strings separated with commas
s: string
b: boolean
f: float
d: int
--------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------
Dataset parameters
ss: dataset.trainSetList = ECCV/piccadilly/; # All the first images of oxford dataset is used for training s: dataset.dataType = ECCV; # the dataset type
--------------------------------------------------------------------------------------
Model parameters
s: model.modelType = Combined; # the network type b: model.bNormalizeInput = 1; # Normalize input to have zero mean and 1 std f: model.fScaleList = np.array([1.0]); # list of scale spaces (small means larger scale)
GHH related
f: model.max_strength = -1; # GHH softmax strength (-1 for hard)
Keypoints
s: model.sDetector = tilde; # network architecture for kp d: model.nFilterSize = 25; # Let's keep the number odd d: model.nPatchSizeKp = 48; # patch size for kp including moving
regions we use something smaller
s: model.sKpNonlinearity = None; # use nonlinearity at end f: model.fScaleList = np.array([1.0]); # list of scale spaces (small means larger scale) f: model.bias_rnd = 0.0; # random noise added to bias f: model.epsilon = 1e-8; # epsilon for tilde
Orientation
s: model.sOrientation = cvpr16; # network architecture for orientation
Descriptor
s: model.sDescriptor = descriptor_imported; # network architecture for desc d: model.nDescInputSize = 64; # Input size to be fed to the descriptor
s: model.descriptor_export_folder = /cvlabdata1/home/trulls-data/kpdesc/torch/export/;
L-> skipped s: model.descriptor_model = new-CNN3-picc-iter-56k.h5; # network configuration s: model.descriptor_input = desc-input; # Descriptor input
--------------------------------------------------------------------------------------
Parameters for patch extraction
automatically determined
f: patch.fMaxScale = np.max(self.model.fScaleList); # asserts make sure this is stored properly f: patch.fRatioScale = (self.model.nPatchSizeKp / 2.0) / 2.0; # to not resize when scale is 2 d: patch.nPatchSize = np.round(self.model.nDescInputSize * self.patch.fRatioScale / 6.0); # the large patch size for data. the desc will look at ratio scale of 6.0
--------------------------------------------------------------------------------------
Validation and test time parameters
d: validation.batch_size = 100; # batch size of the implementation
d: validation.nScaleInterval = 4;
L-> skipped
d: validation.nNMSInterval = 2; # number of intervals we look for
L-> skipped
NMS (3 would mean it goes up one
octave in case of
nScaleInterval=2)
---------------------------------------------------------------------------------
Test Data Module
Traceback (most recent call last): File "compute_descriptor.py", line 111, in
test_data_in = data_module.data_obj(param, image_file_name, kp_file_name)
File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 64, in init
self.load_data(param, image_file_name, kp_file_name)
File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 82, in load_data
pathconf, param, image_file_name, kp_file_name)
File "/home/hoo104/LIFT/python-code/Utils/dataset_tools/test.py", line 135, in load_data_for_set
angle = np.pi / 180.0 * kp[:, IDX_ANGLE] # store angle in radians
IndexError: too many indices for array