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Traceback (most recent call last):
File "", line 1, in
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.2\plugins\python-ce\helpers\pydev_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.2\plugins\python-ce\helpers\pydev_pydev_imps_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "D:/Capsule-Forensics-v2-master/test_binaryffpp.py", line 88, in
classes, class = capnet(x, random=opt.random)
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, kwargs)
File "D:\Capsule-Forensics-v2-master\model_big.py", line 181, in forward
z = self.fea_ext(x)
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, *kwargs)
File "D:\Capsule-Forensics-v2-master\model_big.py", line 84, in forward
outputs = [capsule(x.detach()) for capsule in self.capsules]
File "D:\Capsule-Forensics-v2-master\model_big.py", line 84, in
outputs = [capsule(x.detach()) for capsule in self.capsules]
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(input, kwargs)
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\container.py", line 100, in forward
input = module(input)
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "D:\Capsule-Forensics-v2-master\model_big.py", line 30, in forward
return torch.stack((mean, std), dim=1)
RuntimeError: error in LoadLibraryA
In order to test the model, I downloaded a dataset from kaggle. However testing it presents this very issue. I tried searching the internet for solutions. However, I couldn't find any.
Any clues?
0%| | 0/60 [00:02<?, ?it/s] Traceback (most recent call last): File "", line 1, in
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.2\plugins\python-ce\helpers\pydev_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.2\plugins\python-ce\helpers\pydev_pydev_imps_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "D:/Capsule-Forensics-v2-master/test_binaryffpp.py", line 88, in
classes, class = capnet(x, random=opt.random)
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, kwargs)
File "D:\Capsule-Forensics-v2-master\model_big.py", line 181, in forward
z = self.fea_ext(x)
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, *kwargs)
File "D:\Capsule-Forensics-v2-master\model_big.py", line 84, in forward
outputs = [capsule(x.detach()) for capsule in self.capsules]
File "D:\Capsule-Forensics-v2-master\model_big.py", line 84, in
outputs = [capsule(x.detach()) for capsule in self.capsules]
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward( input, kwargs)
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\container.py", line 100, in forward
input = module(input)
File "C:\Users\vatsa\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "D:\Capsule-Forensics-v2-master\model_big.py", line 30, in forward
return torch.stack((mean, std), dim=1)
RuntimeError: error in LoadLibraryA
In order to test the model, I downloaded a dataset from kaggle. However testing it presents this very issue. I tried searching the internet for solutions. However, I couldn't find any. Any clues?