content: import os
  import math
  import random
  import numpy as np
  import torch
  import cv2
  from torchvision.utils import make_grid
  from datetime import datetime
  #import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
  
  
  os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
  
  
  '''
  # --------------------------------------------
  # Kai Zhang (github: https://github.com/cszn)
  # 03/Mar/2019
  # --------------------------------------------
  # https://github.com/twhui/SRGAN-pyTorch
  # https://github.com/xinntao/BasicSR
  # --------------------------------------------
  '''
  
  
  IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
  
  
  def is_image_file(filename):
      return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
  
  
  def get_timestamp():
      return datetime.now().strftime('%y%m%d-%H%M%S')
  
  
  def imshow(x, title=None, cbar=False, figsize=None):
      plt.figure(figsize=figsize)
      plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
      if title:
          plt.title(title)
      if cbar:
          plt.colorbar()
      plt.show()
  
  
  def surf(Z, cmap='rainbow', figsize=None):
      plt.figure(figsize=figsize)
      ax3 = plt.axes(projection='3d')
  
      w, h = Z.shape[:2]
      xx = np.arange(0,w,1)
      yy = np.arange(0,h,1)
      X, Y = np.meshgrid(xx, yy)
      ax3.plot_surface(X,Y,Z,cmap=cmap)
      #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
      plt.show()
  
  
  '''
  # --------------------------------------------
  # get image pathes
  # --------------------------------------------
  '''
  
  
  def get_image_paths(dataroot):
      paths = None  # return None if dataroot is None
      if dataroot is not None:
          paths = sorted(_get_paths_from_images(dataroot))
      return paths
  
  
  def _get_paths_from_images(path):
      assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
      images = []
      for dirpath, _, fnames in sorted(os.walk(path)):
          for fname in sorted(fnames):
              if is_image_file(fname):
                  img_path = os.path.join(dirpath, fname)
                  images.append(img_path)
      assert images, '{:s} has no valid image file'.format(path)
      return images
  
  
  '''
  # --------------------------------------------
  # split large images into small images 
  # --------------------------------------------
  '''
  
  
  def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
      w, h = img.shape[:2]
      patches = []
      if w > p_max and h > p_max:
          w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
          h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
          w1.append(w-p_size)
          h1.append(h-p_size)
  #        print(w1)
  #        print(h1)
          for i in w1:
              for j in h1:
                  patches.append(img[i:i+p_size, j:j+p_size,:])
      else:
          patches.append(img)
  
      return patches
  
  
  def imssave(imgs, img_path):
      """
      imgs: list, N images of size WxHxC
      """
      img_name, ext = os.path.splitext(os.path.basename(img_path))
  
      for i, img in enumerate(imgs):
          if img.ndim == 3:
              img = img[:, :, [2, 1, 0]]
          new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
          cv2.imwrite(new_path, img)
  
  
  def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
      """
      split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
      and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
      will be splitted.
      Args:
          original_dataroot:
          taget_dataroot:
          p_size: size of small images
          p_overlap: patch size in training is a good choice
          p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
      """
      paths = get_image_paths(original_dataroot)
      for img_path in paths:
          # img_name, ext = os.path.splitext(os.path.basename(img_path))
          img = imread_uint(img_path, n_channels=n_channels)
          patches = patches_from_image(img, p_size, p_overlap, p_max)
          imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
          #if original_dataroot == taget_dataroot:
          #del img_path
  
  '''
  # --------------------------------------------
  # makedir
  # --------------------------------------------
  '''
  
  
  def mkdir(path):
      if not os.path.exists(path):
          os.makedirs(path)
  
  
  def mkdirs(paths):
      if isinstance(paths, str):
          mkdir(paths)
      else:
          for path in paths:
              mkdir(path)
  
  
  def mkdir_and_rename(path):
      if os.path.exists(path):
          new_name = path + '_archived_' + get_timestamp()
          print('Path already exists. Rename it to [{:s}]'.format(new_name))
          os.rename(path, new_name)
      os.makedirs(path)
  
  
  '''
  # --------------------------------------------
  # read image from path
  # opencv is fast, but read BGR numpy image
  # --------------------------------------------
  '''
  
  
  # --------------------------------------------
  # get uint8 image of size HxWxn_channles (RGB)
  # --------------------------------------------
  def imread_uint(path, n_channels=3):
      #  input: path
      # output: HxWx3(RGB or GGG), or HxWx1 (G)
      if n_channels == 1:
          img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
          img = np.expand_dims(img, axis=2)  # HxWx1
      elif n_channels == 3:
          img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
          if img.ndim == 2:
              img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
          else:
              img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
      return img
  
  
  # --------------------------------------------
  # matlab's imwrite
  # --------------------------------------------
  def imsave(img, img_path):
      img = np.squeeze(img)
      if img.ndim == 3:
          img = img[:, :, [2, 1, 0]]
      cv2.imwrite(img_path, img)
  
  def imwrite(img, img_path):
      img = np.squeeze(img)
      if img.ndim == 3:
          img = img[:, :, [2, 1, 0]]
      cv2.imwrite(img_path, img)
  
  
  
  # --------------------------------------------
  # get single image of size HxWxn_channles (BGR)
  # --------------------------------------------
  def read_img(path):
      # read image by cv2
      # return: Numpy float32, HWC, BGR, [0,1]
      img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
      img = img.astype(np.float32) / 255.
      if img.ndim == 2:
          img = np.expand_dims(img, axis=2)
      # some images have 4 channels
      if img.shape[2] > 3:
          img = img[:, :, :3]
      return img
  
  
  '''
  # --------------------------------------------
  # image format conversion
  # --------------------------------------------
  # numpy(single) <--->  numpy(unit)
  # numpy(single) <--->  tensor
  # numpy(unit)   <--->  tensor
  # --------------------------------------------
  '''
  
  
  # --------------------------------------------
  # numpy(single) [0, 1] <--->  numpy(unit)
  # --------------------------------------------
  
  
  def uint2single(img):
  
      return np.float32(img/255.)
  
  
  def single2uint(img):
  
      return np.uint8((img.clip(0, 1)*255.).round())
  
  
  def uint162single(img):
  
      return np.float32(img/65535.)
  
  
  def single2uint16(img):
  
      return np.uint16((img.clip(0, 1)*65535.).round())
  
  
  # --------------------------------------------
  # numpy(unit) (HxWxC or HxW) <--->  tensor
  # --------------------------------------------
  
  
  # convert uint to 4-dimensional torch tensor
  def uint2tensor4(img):
      if img.ndim == 2:
          img = np.expand_dims(img, axis=2)
      return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
  
  
  # convert uint to 3-dimensional torch tensor
  def uint2tensor3(img):
      if img.ndim == 2:
          img = np.expand_dims(img, axis=2)
      return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
  
  
  # convert 2/3/4-dimensional torch tensor to uint
  def tensor2uint(img):
      img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
      if img.ndim == 3:
          img = np.transpose(img, (1, 2, 0))
      return np.uint8((img*255.0).round())
  
  
  # --------------------------------------------
  # numpy(single) (HxWxC) <--->  tensor
  # --------------------------------------------
  
  
  # convert single (HxWxC) to 3-dimensional torch tensor
  def single2tensor3(img):
      return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
  
  
  # convert single (HxWxC) to 4-dimensional torch tensor
  def single2tensor4(img):
      return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
  
  
  # convert torch tensor to single
  def tensor2single(img):
      img = img.data.squeeze().float().cpu().numpy()
      if img.ndim == 3:
          img = np.transpose(img, (1, 2, 0))
  
      return img
  
  # convert torch tensor to single
  def tensor2single3(img):
      img = img.data.squeeze().float().cpu().numpy()
      if img.ndim == 3:
          img = np.transpose(img, (1, 2, 0))
      elif img.ndim == 2:
          img = np.expand_dims(img, axis=2)
      return img
  
  
  def single2tensor5(img):
      return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
  
  
  def single32tensor5(img):
      return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
  
  
  def single42tensor4(img):
      return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
  
  
  # from skimage.io import imread, imsave
  def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
      '''
      Converts a torch Tensor into an image Numpy array of BGR channel order
      Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
      Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
      '''
      tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
      tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
      n_dim = tensor.dim()
      if n_dim == 4:
          n_img = len(tensor)
          img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
          img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
      elif n_dim == 3:
          img_np = tensor.numpy()
          img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
      elif n_dim == 2:
          img_np = tensor.numpy()
      else:
          raise TypeError(
              'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
      if out_type == np.uint8:
          img_np = (img_np * 255.0).round()
          # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
      return img_np.astype(out_type)
  
  
  '''
  # --------------------------------------------
  # Augmentation, flipe and/or rotate
  # --------------------------------------------
  # The following two are enough.
  # (1) augmet_img: numpy image of WxHxC or WxH
  # (2) augment_img_tensor4: tensor image 1xCxWxH
  # --------------------------------------------
  '''
  
  
  def augment_img(img, mode=0):
      '''Kai Zhang (github: https://github.com/cszn)
      '''
      if mode == 0:
          return img
      elif mode == 1:
          return np.flipud(np.rot90(img))
      elif mode == 2:
          return np.flipud(img)
      elif mode == 3:
          return np.rot90(img, k=3)
      elif mode == 4:
          return np.flipud(np.rot90(img, k=2))
      elif mode == 5:
          return np.rot90(img)
      elif mode == 6:
          return np.rot90(img, k=2)
      elif mode == 7:
          return np.flipud(np.rot90(img, k=3))
  
  
  def augment_img_tensor4(img, mode=0):
      '''Kai Zhang (github: https://github.com/cszn)
      '''
      if mode == 0:
          return img
      elif mode == 1:
          return img.rot90(1, [2, 3]).flip([2])
      elif mode == 2:
          return img.flip([2])
      elif mode == 3:
          return img.rot90(3, [2, 3])
      elif mode == 4:
          return img.rot90(2, [2, 3]).flip([2])
      elif mode == 5:
          return img.rot90(1, [2, 3])
      elif mode == 6:
          return img.rot90(2, [2, 3])
      elif mode == 7:
          return img.rot90(3, [2, 3]).flip([2])
  
  
  def augment_img_tensor(img, mode=0):
      '''Kai Zhang (github: https://github.com/cszn)
      '''
      img_size = img.size()
      img_np = img.data.cpu().numpy()
      if len(img_size) == 3:
          img_np = np.transpose(img_np, (1, 2, 0))
      elif len(img_size) == 4:
          img_np = np.transpose(img_np, (2, 3, 1, 0))
      img_np = augment_img(img_np, mode=mode)
      img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
      if len(img_size) == 3:
          img_tensor = img_tensor.permute(2, 0, 1)
      elif len(img_size) == 4:
          img_tensor = img_tensor.permute(3, 2, 0, 1)
  
      return img_tensor.type_as(img)
  
  
  def augment_img_np3(img, mode=0):
      if mode == 0:
          return img
      elif mode == 1:
          return img.transpose(1, 0, 2)
      elif mode == 2:
          return img[::-1, :, :]
      elif mode == 3:
          img = img[::-1, :, :]
          img = img.transpose(1, 0, 2)
          return img
      elif mode == 4:
          return img[:, ::-1, :]
      elif mode == 5:
          img = img[:, ::-1, :]
          img = img.transpose(1, 0, 2)
          return img
      elif mode == 6:
          img = img[:, ::-1, :]
          img = img[::-1, :, :]
          return img
      elif mode == 7:
          img = img[:, ::-1, :]
          img = img[::-1, :, :]
          img = img.transpose(1, 0, 2)
          return img
  
  
  def augment_imgs(img_list, hflip=True, rot=True):
      # horizontal flip OR rotate
      hflip = hflip and random.random() < 0.5
      vflip = rot and random.random() < 0.5
      rot90 = rot and random.random() < 0.5
  
      def _augment(img):
          if hflip:
              img = img[:, ::-1, :]
          if vflip:
              img = img[::-1, :, :]
          if rot90:
              img = img.transpose(1, 0, 2)
          return img
  
      return [_augment(img) for img in img_list]
  
  
  '''
  # --------------------------------------------
  # modcrop and shave
  # --------------------------------------------
  '''
  
  
  def modcrop(img_in, scale):
      # img_in: Numpy, HWC or HW
      img = np.copy(img_in)
      if img.ndim == 2:
          H, W = img.shape
          H_r, W_r = H % scale, W % scale
          img = img[:H - H_r, :W - W_r]
      elif img.ndim == 3:
          H, W, C = img.shape
          H_r, W_r = H % scale, W % scale
          img = img[:H - H_r, :W - W_r, :]
      else:
          raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
      return img
  
  
  def shave(img_in, border=0):
      # img_in: Numpy, HWC or HW
      img = np.copy(img_in)
      h, w = img.shape[:2]
      img = img[border:h-border, border:w-border]
      return img
  
  
  '''
  # --------------------------------------------
  # image processing process on numpy image
  # channel_convert(in_c, tar_type, img_list):
  # rgb2ycbcr(img, only_y=True):
  # bgr2ycbcr(img, only_y=True):
  # ycbcr2rgb(img):
  # --------------------------------------------
  '''
  
  
  def rgb2ycbcr(img, only_y=True):
      '''same as matlab rgb2ycbcr
      only_y: only return Y channel
      Input:
          uint8, [0, 255]
          float, [0, 1]
      '''
      in_img_type = img.dtype
      img.astype(np.float32)
      if in_img_type != np.uint8:
          img *= 255.
      # convert
      if only_y:
          rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
      else:
          rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
                                [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
      if in_img_type == np.uint8:
          rlt = rlt.round()
      else:
          rlt /= 255.
      return rlt.astype(in_img_type)
  
  
  def ycbcr2rgb(img):
      '''same as matlab ycbcr2rgb
      Input:
          uint8, [0, 255]
          float, [0, 1]
      '''
      in_img_type = img.dtype
      img.astype(np.float32)
      if in_img_type != np.uint8:
          img *= 255.
      # convert
      rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
                            [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
      if in_img_type == np.uint8:
          rlt = rlt.round()
      else:
          rlt /= 255.
      return rlt.astype(in_img_type)
  
  
  def bgr2ycbcr(img, only_y=True):
      '''bgr version of rgb2ycbcr
      only_y: only return Y channel
      Input:
          uint8, [0, 255]
          float, [0, 1]
      '''
      in_img_type = img.dtype
      img.astype(np.float32)
      if in_img_type != np.uint8:
          img *= 255.
      # convert
      if only_y:
          rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
      else:
          rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
                                [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
      if in_img_type == np.uint8:
          rlt = rlt.round()
      else:
          rlt /= 255.
      return rlt.astype(in_img_type)
  
  
  def channel_convert(in_c, tar_type, img_list):
      # conversion among BGR, gray and y
      if in_c == 3 and tar_type == 'gray':  # BGR to gray
          gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
          return [np.expand_dims(img, axis=2) for img in gray_list]
      elif in_c == 3 and tar_type == 'y':  # BGR to y
          y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
          return [np.expand_dims(img, axis=2) for img in y_list]
      elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
          return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
      else:
          return img_list
  
  
  '''
  # --------------------------------------------
  # metric, PSNR and SSIM
  # --------------------------------------------
  '''
  
  
  # --------------------------------------------
  # PSNR
  # --------------------------------------------
  def calculate_psnr(img1, img2, border=0):
      # img1 and img2 have range [0, 255]
      #img1 = img1.squeeze()
      #img2 = img2.squeeze()
      if not img1.shape == img2.shape:
          raise ValueError('Input images must have the same dimensions.')
      h, w = img1.shape[:2]
      img1 = img1[border:h-border, border:w-border]
      img2 = img2[border:h-border, border:w-border]
  
      img1 = img1.astype(np.float64)
      img2 = img2.astype(np.float64)
      mse = np.mean((img1 - img2)**2)
      if mse == 0:
          return float('inf')
      return 20 * math.log10(255.0 / math.sqrt(mse))
  
  
  # --------------------------------------------
  # SSIM
  # --------------------------------------------
  def calculate_ssim(img1, img2, border=0):
      '''calculate SSIM
      the same outputs as MATLAB's
      img1, img2: [0, 255]
      '''
      #img1 = img1.squeeze()
      #img2 = img2.squeeze()
      if not img1.shape == img2.shape:
          raise ValueError('Input images must have the same dimensions.')
      h, w = img1.shape[:2]
      img1 = img1[border:h-border, border:w-border]
      img2 = img2[border:h-border, border:w-border]
  
      if img1.ndim == 2:
          return ssim(img1, img2)
      elif img1.ndim == 3:
          if img1.shape[2] == 3:
              ssims = []
              for i in range(3):
                  ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
              return np.array(ssims).mean()
          elif img1.shape[2] == 1:
              return ssim(np.squeeze(img1), np.squeeze(img2))
      else:
          raise ValueError('Wrong input image dimensions.')
  
  
  def ssim(img1, img2):
      C1 = (0.01 * 255)**2
      C2 = (0.03 * 255)**2
  
      img1 = img1.astype(np.float64)
      img2 = img2.astype(np.float64)
      kernel = cv2.getGaussianKernel(11, 1.5)
      window = np.outer(kernel, kernel.transpose())
  
      mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
      mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
      mu1_sq = mu1**2
      mu2_sq = mu2**2
      mu1_mu2 = mu1 * mu2
      sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
      sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
      sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
  
      ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                                              (sigma1_sq + sigma2_sq + C2))
      return ssim_map.mean()
  
  
  '''
  # --------------------------------------------
  # matlab's bicubic imresize (numpy and torch) [0, 1]
  # --------------------------------------------
  '''
  
  
  # matlab 'imresize' function, now only support 'bicubic'
  def cubic(x):
      absx = torch.abs(x)
      absx2 = absx**2
      absx3 = absx**3
      return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
          (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
  
  
  def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
      if (scale < 1) and (antialiasing):
          # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
          kernel_width = kernel_width / scale
  
      # Output-space coordinates
      x = torch.linspace(1, out_length, out_length)
  
      # Input-space coordinates. Calculate the inverse mapping such that 0.5
      # in output space maps to 0.5 in input space, and 0.5+scale in output
      # space maps to 1.5 in input space.
      u = x / scale + 0.5 * (1 - 1 / scale)
  
      # What is the left-most pixel that can be involved in the computation?
      left = torch.floor(u - kernel_width / 2)
  
      # What is the maximum number of pixels that can be involved in the
      # computation?  Note: it's OK to use an extra pixel here; if the
      # corresponding weights are all zero, it will be eliminated at the end
      # of this function.
      P = math.ceil(kernel_width) + 2
  
      # The indices of the input pixels involved in computing the k-th output
      # pixel are in row k of the indices matrix.
      indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
          1, P).expand(out_length, P)
  
      # The weights used to compute the k-th output pixel are in row k of the
      # weights matrix.
      distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
      # apply cubic kernel
      if (scale < 1) and (antialiasing):
          weights = scale * cubic(distance_to_center * scale)
      else:
          weights = cubic(distance_to_center)
      # Normalize the weights matrix so that each row sums to 1.
      weights_sum = torch.sum(weights, 1).view(out_length, 1)
      weights = weights / weights_sum.expand(out_length, P)
  
      # If a column in weights is all zero, get rid of it. only consider the first and last column.
      weights_zero_tmp = torch.sum((weights == 0), 0)
      if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
          indices = indices.narrow(1, 1, P - 2)
          weights = weights.narrow(1, 1, P - 2)
      if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
          indices = indices.narrow(1, 0, P - 2)
          weights = weights.narrow(1, 0, P - 2)
      weights = weights.contiguous()
      indices = indices.contiguous()
      sym_len_s = -indices.min() + 1
      sym_len_e = indices.max() - in_length
      indices = indices + sym_len_s - 1
      return weights, indices, int(sym_len_s), int(sym_len_e)
  
  
  # --------------------------------------------
  # imresize for tensor image [0, 1]
  # --------------------------------------------
  def imresize(img, scale, antialiasing=True):
      # Now the scale should be the same for H and W
      # input: img: pytorch tensor, CHW or HW [0,1]
      # output: CHW or HW [0,1] w/o round
      need_squeeze = True if img.dim() == 2 else False
      if need_squeeze:
          img.unsqueeze_(0)
      in_C, in_H, in_W = img.size()
      out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
      kernel_width = 4
      kernel = 'cubic'
  
      # Return the desired dimension order for performing the resize.  The
      # strategy is to perform the resize first along the dimension with the
      # smallest scale factor.
      # Now we do not support this.
  
      # get weights and indices
      weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
          in_H, out_H, scale, kernel, kernel_width, antialiasing)
      weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
          in_W, out_W, scale, kernel, kernel_width, antialiasing)
      # process H dimension
      # symmetric copying
      img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
      img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
  
      sym_patch = img[:, :sym_len_Hs, :]
      inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
      sym_patch_inv = sym_patch.index_select(1, inv_idx)
      img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
  
      sym_patch = img[:, -sym_len_He:, :]
      inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
      sym_patch_inv = sym_patch.index_select(1, inv_idx)
      img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
  
      out_1 = torch.FloatTensor(in_C, out_H, in_W)
      kernel_width = weights_H.size(1)
      for i in range(out_H):
          idx = int(indices_H[i][0])
          for j in range(out_C):
              out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
  
      # process W dimension
      # symmetric copying
      out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
      out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
  
      sym_patch = out_1[:, :, :sym_len_Ws]
      inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
      sym_patch_inv = sym_patch.index_select(2, inv_idx)
      out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
  
      sym_patch = out_1[:, :, -sym_len_We:]
      inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
      sym_patch_inv = sym_patch.index_select(2, inv_idx)
      out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
  
      out_2 = torch.FloatTensor(in_C, out_H, out_W)
      kernel_width = weights_W.size(1)
      for i in range(out_W):
          idx = int(indices_W[i][0])
          for j in range(out_C):
              out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
      if need_squeeze:
          out_2.squeeze_()
      return out_2
  
  
  # --------------------------------------------
  # imresize for numpy image [0, 1]
  # --------------------------------------------
  def imresize_np(img, scale, antialiasing=True):
      # Now the scale should be the same for H and W
      # input: img: Numpy, HWC or HW [0,1]
      # output: HWC or HW [0,1] w/o round
      img = torch.from_numpy(img)
      need_squeeze = True if img.dim() == 2 else False
      if need_squeeze:
          img.unsqueeze_(2)
  
      in_H, in_W, in_C = img.size()
      out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
      kernel_width = 4
      kernel = 'cubic'
  
      # Return the desired dimension order for performing the resize.  The
      # strategy is to perform the resize first along the dimension with the
      # smallest scale factor.
      # Now we do not support this.
  
      # get weights and indices
      weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
          in_H, out_H, scale, kernel, kernel_width, antialiasing)
      weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
          in_W, out_W, scale, kernel, kernel_width, antialiasing)
      # process H dimension
      # symmetric copying
      img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
      img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
  
      sym_patch = img[:sym_len_Hs, :, :]
      inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
      sym_patch_inv = sym_patch.index_select(0, inv_idx)
      img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
  
      sym_patch = img[-sym_len_He:, :, :]
      inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
      sym_patch_inv = sym_patch.index_select(0, inv_idx)
      img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
  
      out_1 = torch.FloatTensor(out_H, in_W, in_C)
      kernel_width = weights_H.size(1)
      for i in range(out_H):
          idx = int(indices_H[i][0])
          for j in range(out_C):
              out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
  
      # process W dimension
      # symmetric copying
      out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
      out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
  
      sym_patch = out_1[:, :sym_len_Ws, :]
      inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
      sym_patch_inv = sym_patch.index_select(1, inv_idx)
      out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
  
      sym_patch = out_1[:, -sym_len_We:, :]
      inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
      sym_patch_inv = sym_patch.index_select(1, inv_idx)
      out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
  
      out_2 = torch.FloatTensor(out_H, out_W, in_C)
      kernel_width = weights_W.size(1)
      for i in range(out_W):
          idx = int(indices_W[i][0])
          for j in range(out_C):
              out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
      if need_squeeze:
          out_2.squeeze_()
  
      return out_2.numpy()
  
  
  if __name__ == '__main__':
      print('---')
  #    img = imread_uint('test.bmp', 3)
  #    img = uint2single(img)
  #    img_bicubic = imresize_np(img, 1/4)