pytorch dataloader stucked if using opencv resize method












1















I can run all the cells of the tutorial notebook of Pytorch about dataloading (pytorch tutorial).
But when I use OpenCV in place of Skimage to resize the image, the dataloader gets stuck, i.e nothing happens.



In the Rescale class:



class Rescale(object):

    .....

    def __call__(self, sample):

       ....

       #img = transform.resize(image, (new_h, new_w))

       img = cv2.resize(image, (new_h, new_w))

       .....


The dataloader and the for loop are defined with:



dataloader = DataLoader(transformed_dataset, batch_size=4,

                        shuffle=True, num_workers=4)



for i_batch, sample_batched in enumerate(dataloader):

    print(i_batch, sample_batched['image'].size(),

          sample_batched['landmarks'].size())


I can get the iterator to print something if num_workers=0. It looks like opencv does not play well with the Multiprocessing of pytorch.
I would really prefer to use same package to transform the images at train time and test time (and I am already using OpenCV for the image rescale at test time).
Any suggestions would be greatly appreciated.






python-2.7 opencv multiprocessing pytorch scikit-image







share|improve this question























  • Try cv2.ocl.setUseOpenCL(False)

    – Kinght 金
    Jan 3 at 1:14
















1















I can run all the cells of the tutorial notebook of Pytorch about dataloading (pytorch tutorial).
But when I use OpenCV in place of Skimage to resize the image, the dataloader gets stuck, i.e nothing happens.



In the Rescale class:



class Rescale(object):

    .....

    def __call__(self, sample):

       ....

       #img = transform.resize(image, (new_h, new_w))

       img = cv2.resize(image, (new_h, new_w))

       .....


The dataloader and the for loop are defined with:



dataloader = DataLoader(transformed_dataset, batch_size=4,

                        shuffle=True, num_workers=4)



for i_batch, sample_batched in enumerate(dataloader):

    print(i_batch, sample_batched['image'].size(),

          sample_batched['landmarks'].size())


I can get the iterator to print something if num_workers=0. It looks like opencv does not play well with the Multiprocessing of pytorch.
I would really prefer to use same package to transform the images at train time and test time (and I am already using OpenCV for the image rescale at test time).
Any suggestions would be greatly appreciated.






python-2.7 opencv multiprocessing pytorch scikit-image







share|improve this question























  • Try cv2.ocl.setUseOpenCL(False)

    – Kinght 金
    Jan 3 at 1:14














1












1








1








I can run all the cells of the tutorial notebook of Pytorch about dataloading (pytorch tutorial).
But when I use OpenCV in place of Skimage to resize the image, the dataloader gets stuck, i.e nothing happens.



In the Rescale class:



class Rescale(object):

    .....

    def __call__(self, sample):

       ....

       #img = transform.resize(image, (new_h, new_w))

       img = cv2.resize(image, (new_h, new_w))

       .....


The dataloader and the for loop are defined with:



dataloader = DataLoader(transformed_dataset, batch_size=4,

                        shuffle=True, num_workers=4)



for i_batch, sample_batched in enumerate(dataloader):

    print(i_batch, sample_batched['image'].size(),

          sample_batched['landmarks'].size())


I can get the iterator to print something if num_workers=0. It looks like opencv does not play well with the Multiprocessing of pytorch.
I would really prefer to use same package to transform the images at train time and test time (and I am already using OpenCV for the image rescale at test time).
Any suggestions would be greatly appreciated.






python-2.7 opencv multiprocessing pytorch scikit-image







share|improve this question














I can run all the cells of the tutorial notebook of Pytorch about dataloading (pytorch tutorial).
But when I use OpenCV in place of Skimage to resize the image, the dataloader gets stuck, i.e nothing happens.



In the Rescale class:



class Rescale(object):

    .....

    def __call__(self, sample):

       ....

       #img = transform.resize(image, (new_h, new_w))

       img = cv2.resize(image, (new_h, new_w))

       .....


The dataloader and the for loop are defined with:



dataloader = DataLoader(transformed_dataset, batch_size=4,

                        shuffle=True, num_workers=4)



for i_batch, sample_batched in enumerate(dataloader):

    print(i_batch, sample_batched['image'].size(),

          sample_batched['landmarks'].size())


I can get the iterator to print something if num_workers=0. It looks like opencv does not play well with the Multiprocessing of pytorch.
I would really prefer to use same package to transform the images at train time and test time (and I am already using OpenCV for the image rescale at test time).
Any suggestions would be greatly appreciated.






python-2.7 opencv multiprocessing pytorch scikit-image




python-2.7 opencv multiprocessing pytorch scikit-image






share|improve this question













share|improve this question











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asked Jan 2 at 22:11









JMarcJMarc

445615




445615













  • Try cv2.ocl.setUseOpenCL(False)

    – Kinght 金
    Jan 3 at 1:14



















  • Try cv2.ocl.setUseOpenCL(False)

    – Kinght 金
    Jan 3 at 1:14

















Try cv2.ocl.setUseOpenCL(False)

– Kinght 金
Jan 3 at 1:14





Try cv2.ocl.setUseOpenCL(False)

– Kinght 金
Jan 3 at 1:14












1 Answer
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I had a very similar problem and that's how I solved it:



when you import cv2 set cv2.setNumThreads(0)
and then you can set num_workers>0 in the dataloader in PyTorch.



Seems like OpenCV tries to multithread and somewhere something goes into a deadlock.



Hope it helps.






share|improve this answer
























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    1 Answer
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    active

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    2














    I had a very similar problem and that's how I solved it:



    when you import cv2 set cv2.setNumThreads(0)
    and then you can set num_workers>0 in the dataloader in PyTorch.



    Seems like OpenCV tries to multithread and somewhere something goes into a deadlock.



    Hope it helps.






    share|improve this answer




























      2














      I had a very similar problem and that's how I solved it:



      when you import cv2 set cv2.setNumThreads(0)
      and then you can set num_workers>0 in the dataloader in PyTorch.



      Seems like OpenCV tries to multithread and somewhere something goes into a deadlock.



      Hope it helps.






      share|improve this answer


























        2












        2








        2







        I had a very similar problem and that's how I solved it:



        when you import cv2 set cv2.setNumThreads(0)
        and then you can set num_workers>0 in the dataloader in PyTorch.



        Seems like OpenCV tries to multithread and somewhere something goes into a deadlock.



        Hope it helps.






        share|improve this answer













        I had a very similar problem and that's how I solved it:



        when you import cv2 set cv2.setNumThreads(0)
        and then you can set num_workers>0 in the dataloader in PyTorch.



        Seems like OpenCV tries to multithread and somewhere something goes into a deadlock.



        Hope it helps.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Feb 9 at 3:25









        Aidas LiaudanskasAidas Liaudanskas

        214




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