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Configuration of masks in PixelCNN



2019 Community Moderator ElectionQuestions about CNN: weights and biasesCustom loss function which is included gradient in KerasIntuitive explanation of Convolutional layers in Deep CNNDetermining size of FC layer after Conv layer in PyTorchWhat is the purpose of a 1x1 convolutional layer?Wrangling data for CNNwhat happens to the depth channels when convolved by multiple filters in a cnn (keras, tensorflow)CNN architecture design guidelines when doing multilabel classification of 1K possible “easy” classesWhat is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?What are the possible values of a filter in a CNN?










1












$begingroup$


I have trouble understanding the masks in PixelCNN 1.



enter image description hereenter image description here



Here for A, each channel is connected to prior channels. For B, channels are also connected to themselves. My problem is in first layer when I go from 3 channels to number of filters. Let's say I have 128 filters, how is the mask then implemented? What I implemented so far with the assumption that there is same number of channels in each layer is:



H = KERNEL_SIZE[0]
W = KERNEL_SIZE[1]
N = OUT_CHANNEL
M = IN_CHANNEL

# implement context mask
mask = np.zeros((N, M, H, W))
mask[:,:,:(H//2)] = 1
mask[:,:,(H//2),range((W//2))] = 1

# implement channel mask
if mask_type == 'A':
mask[np.triu_indices(N, k=0, m=M)] = 0
elif mask_type == 'B':
mask[np.triu_indices(N, k=1, m=M)] = 0









share|improve this question







New contributor




Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$
















    1












    $begingroup$


    I have trouble understanding the masks in PixelCNN 1.



    enter image description hereenter image description here



    Here for A, each channel is connected to prior channels. For B, channels are also connected to themselves. My problem is in first layer when I go from 3 channels to number of filters. Let's say I have 128 filters, how is the mask then implemented? What I implemented so far with the assumption that there is same number of channels in each layer is:



    H = KERNEL_SIZE[0]
    W = KERNEL_SIZE[1]
    N = OUT_CHANNEL
    M = IN_CHANNEL

    # implement context mask
    mask = np.zeros((N, M, H, W))
    mask[:,:,:(H//2)] = 1
    mask[:,:,(H//2),range((W//2))] = 1

    # implement channel mask
    if mask_type == 'A':
    mask[np.triu_indices(N, k=0, m=M)] = 0
    elif mask_type == 'B':
    mask[np.triu_indices(N, k=1, m=M)] = 0









    share|improve this question







    New contributor




    Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      1












      1








      1





      $begingroup$


      I have trouble understanding the masks in PixelCNN 1.



      enter image description hereenter image description here



      Here for A, each channel is connected to prior channels. For B, channels are also connected to themselves. My problem is in first layer when I go from 3 channels to number of filters. Let's say I have 128 filters, how is the mask then implemented? What I implemented so far with the assumption that there is same number of channels in each layer is:



      H = KERNEL_SIZE[0]
      W = KERNEL_SIZE[1]
      N = OUT_CHANNEL
      M = IN_CHANNEL

      # implement context mask
      mask = np.zeros((N, M, H, W))
      mask[:,:,:(H//2)] = 1
      mask[:,:,(H//2),range((W//2))] = 1

      # implement channel mask
      if mask_type == 'A':
      mask[np.triu_indices(N, k=0, m=M)] = 0
      elif mask_type == 'B':
      mask[np.triu_indices(N, k=1, m=M)] = 0









      share|improve this question







      New contributor




      Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I have trouble understanding the masks in PixelCNN 1.



      enter image description hereenter image description here



      Here for A, each channel is connected to prior channels. For B, channels are also connected to themselves. My problem is in first layer when I go from 3 channels to number of filters. Let's say I have 128 filters, how is the mask then implemented? What I implemented so far with the assumption that there is same number of channels in each layer is:



      H = KERNEL_SIZE[0]
      W = KERNEL_SIZE[1]
      N = OUT_CHANNEL
      M = IN_CHANNEL

      # implement context mask
      mask = np.zeros((N, M, H, W))
      mask[:,:,:(H//2)] = 1
      mask[:,:,(H//2),range((W//2))] = 1

      # implement channel mask
      if mask_type == 'A':
      mask[np.triu_indices(N, k=0, m=M)] = 0
      elif mask_type == 'B':
      mask[np.triu_indices(N, k=1, m=M)] = 0






      cnn






      share|improve this question







      New contributor




      Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question






      New contributor




      Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked Mar 21 at 12:43









      Oğuz ŞerbetciOğuz Şerbetci

      1061




      1061




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      Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      New contributor





      Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      Oğuz Şerbetci is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.




















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