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Exception in thread Thread-1: ValueError: signal number 32 out of range #30

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moldach opened this issue Mar 22, 2021 · 2 comments
Open

Exception in thread Thread-1: ValueError: signal number 32 out of range #30

moldach opened this issue Mar 22, 2021 · 2 comments

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@moldach
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moldach commented Mar 22, 2021

I had trouble installing on a HPC with access to GPUs (#29 ) so I've now tried to install on my local laptop (without GPU support). Using python 3.8.0 I still get the same errors trying to install requirements.txt and there are a few dependencies that were not mentioned in this file, namely: Cython, torchvision and opencv-python:

modified requirements.txt:

#torch==1.3.0.post2
torch
pytorch-ssim==0.1
numpy
scikit-image
tqdm
#pytorch-ssim==0.1
#numpy==1.16.4
#scikit-image==0.15.0
#tqdm==4.37.0
Cython
torchvision
opencv-python

After successfully installing pyflow via:

cd pyflow/
python setup.py build_ext -i  # build pyflow
python demo.py                # to make sure pyflow works
cp pyflow*.so ..

I try to run the example iSeeBetterTest.py but I get the following error:

(venv) mtg@mtg-ThinkPad-P53:~/iSeeBetter$ python3 iSeeBetterTest.py 
Namespace(chop_forward=False, data_dir='./Vid4', debug=False, file_list='foliage_test.txt', future_frame=True, gpu_mode=False, gpus=0, model='weights/netG_epoch_4_1.pth', model_type='RBPN', nFrames=7, other_dataset=True, output='Results/', residual=False, seed=123, testBatchSize=1, threads=1, upscale_factor=4, upscale_only=False)
==> Loading datasets
==> Building model  RBPN
[    INFO] ------------- iSeeBetter Network Architecture -------------
[    INFO] ----------------- Generator Architecture ------------------
[    INFO] Net(
  (feat0): ConvBlock(
    (conv): Conv2d(3, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (act): PReLU(num_parameters=1)
  )
  (feat1): ConvBlock(
    (conv): Conv2d(8, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (act): PReLU(num_parameters=1)
  )
  (DBPN): Net(
    (feat1): ConvBlock(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (up1): UpBlock(
      (up_conv1): DeconvBlock(
        (deconv): ConvTranspose2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (up_conv2): ConvBlock(
        (conv): Conv2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (up_conv3): DeconvBlock(
        (deconv): ConvTranspose2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
    )
    (down1): DownBlock(
      (down_conv1): ConvBlock(
        (conv): Conv2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (down_conv2): DeconvBlock(
        (deconv): ConvTranspose2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (down_conv3): ConvBlock(
        (conv): Conv2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
    )
    (up2): UpBlock(
      (up_conv1): DeconvBlock(
        (deconv): ConvTranspose2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (up_conv2): ConvBlock(
        (conv): Conv2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (up_conv3): DeconvBlock(
        (deconv): ConvTranspose2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
    )
    (down2): DownBlock(
      (down_conv1): ConvBlock(
        (conv): Conv2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (down_conv2): DeconvBlock(
        (deconv): ConvTranspose2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (down_conv3): ConvBlock(
        (conv): Conv2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
    )
    (up3): UpBlock(
      (up_conv1): DeconvBlock(
        (deconv): ConvTranspose2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (up_conv2): ConvBlock(
        (conv): Conv2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
      (up_conv3): DeconvBlock(
        (deconv): ConvTranspose2d(64, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
        (act): PReLU(num_parameters=1)
      )
    )
    (output): ConvBlock(
      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))
    )
  )
  (res_feat1): Sequential(
    (0): ResnetBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (1): ResnetBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (2): ResnetBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (3): ResnetBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (4): ResnetBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (5): DeconvBlock(
      (deconv): ConvTranspose2d(256, 64, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
      (act): PReLU(num_parameters=1)
    )
  )
  (res_feat2): Sequential(
    (0): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (1): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (2): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (3): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (4): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (5): ConvBlock(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
  )
  (res_feat3): Sequential(
    (0): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (1): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (2): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (3): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (4): ResnetBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): PReLU(num_parameters=1)
    )
    (5): ConvBlock(
      (conv): Conv2d(64, 256, kernel_size=(8, 8), stride=(4, 4), padding=(2, 2))
      (act): PReLU(num_parameters=1)
    )
  )
  (output): ConvBlock(
    (conv): Conv2d(384, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  )
)
[    INFO] Total number of parameters: 12771943
Pre-trained SR model loaded from: weights/netG_epoch_4_1.pth
Exception in thread Thread-1:
Traceback (most recent call last):
  File "/home/mtg/.pyenv/versions/3.6.3/lib/python3.6/threading.py", line 916, in _bootstrap_inner
    self.run()
  File "/home/mtg/.pyenv/versions/3.6.3/lib/python3.6/threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)
  File "/home/mtg/.pyenv/versions/3.6.3/lib/python3.6/multiprocessing/resource_sharer.py", line 139, in _serve
    signal.pthread_sigmask(signal.SIG_BLOCK, range(1, signal.NSIG))
  File "/home/mtg/.pyenv/versions/3.6.3/lib/python3.6/signal.py", line 60, in pthread_sigmask
    sigs_set = _signal.pthread_sigmask(how, mask)
ValueError: signal number 32 out of range

Any idea what the issue is here?
@diegocaumont
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Facing the same issue...

@TheOmega-hash
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same erro here!

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@moldach @TheOmega-hash @diegocaumont