(imgs, num_images)
return rev_imgs
Inverse preprocessing of the batch of images.
       Add the mean vector and convert from BGR to RGB.
       
    Args:
      imgs: batch of input images.
      num_images: number of images to apply the inverse transformations on.
      img_mean: vector of mean colour values.
  
    Returns:
      The batch of the size num_images with the same spatial dimensions as the input.
Inverse preprocessing of the batch of images.
       Add the mean vector and convert from BGR to RGB.
       
    Args:
      imgs: batch of input images.
      num_images: number of images to apply the inverse transformations on.
      img_mean: vector of mean colour values.
  
    Returns:
      The batch of the size num_images with the same spatial dimensions as the input.
Inverse preprocessing of the batch of images . Add the mean vector and convert from BGR to RGB . Args : imgs : batch of input images . num_images : number of images to apply the inverse transformations on . img_mean : vector of mean colour values . Returns : The batch of the size num_images with the same spatial dimensions as the input .
def inv_preprocess(imgs, num_images):
    """Inverse preprocessing of the batch of images.
       Add the mean vector and convert from BGR to RGB.
       
    Args:
      imgs: batch of input images.
      num_images: number of images to apply the inverse transformations on.
      img_mean: vector of mean colour values.
  
    Returns:
      The batch of the size num_images with the same spatial dimensions as the input.
    """
    rev_imgs = imgs[:num_images].clone().cpu().data
    rev_normalize = NormalizeInverse(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    for i in range(num_images):
        rev_imgs[i] = rev_normalize(rev_imgs[i])

    return rev_imgs
def inv_preprocess ( imgs , num_images ) : rev_imgs = imgs [ : num_images ] . clone ( ) . cpu ( ) . data rev_normalize = NormalizeInverse ( mean = [ 0.485 , 0.456 , 0.406 ] , std = [ 0.229 , 0.224 , 0.225 ] ) for i in range ( num_images ) : rev_imgs [ i ] = rev_normalize ( rev_imgs [ i ] ) return rev_imgs