text: import random
  import numpy as np
  import torch
  import torch.utils.data as data
  import utils.utils_image as util
  
  
  class DatasetFDnCNN(data.Dataset):
      """
      # -----------------------------------------
      # Get L/H/M for denosing on AWGN with a range of sigma.
      # Only dataroot_H is needed.
      # -----------------------------------------
      # e.g., FDnCNN, H = f(cat(L, M)), M is noise level map
      # -----------------------------------------
      """
  
      def __init__(self, opt):
          super(DatasetFDnCNN, self).__init__()
          self.opt = opt
          self.n_channels = opt['n_channels'] if opt['n_channels'] else 3
          self.patch_size = self.opt['H_size'] if opt['H_size'] else 64
          self.sigma = opt['sigma'] if opt['sigma'] else [0, 75]
          self.sigma_min, self.sigma_max = self.sigma[0], self.sigma[1]
          self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else 25
  
          # -------------------------------------
          # get the path of H, return None if input is None
          # -------------------------------------
          self.paths_H = util.get_image_paths(opt['dataroot_H'])
  
      def __getitem__(self, index):
          # -------------------------------------
          # get H image
          # -------------------------------------
          H_path = self.paths_H[index]
          img_H = util.imread_uint(H_path, self.n_channels)
  
          L_path = H_path
  
          if self.opt['phase'] == 'train':
              """
              # --------------------------------
              # get L/H/M patch pairs
              # --------------------------------
              """
              H, W = img_H.shape[:2]
  
              # ---------------------------------
              # randomly crop the patch
              # ---------------------------------
              rnd_h = random.randint(0, max(0, H - self.patch_size))
              rnd_w = random.randint(0, max(0, W - self.patch_size))
              patch_H = img_H[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :]
  
              # ---------------------------------
              # augmentation - flip, rotate
              # ---------------------------------
              mode = random.randint(0, 7)
              patch_H = util.augment_img(patch_H, mode=mode)
  
              # ---------------------------------
              # HWC to CHW, numpy(uint) to tensor
              # ---------------------------------
              img_H = util.uint2tensor3(patch_H)
              img_L = img_H.clone()
  
              # ---------------------------------
              # get noise level
              # ---------------------------------
              # noise_level = torch.FloatTensor([np.random.randint(self.sigma_min, self.sigma_max)])/255.0
              noise_level = torch.FloatTensor([np.random.uniform(self.sigma_min, self.sigma_max)])/255.0
  
              noise_level_map = torch.ones((1, img_L.size(1), img_L.size(2))).mul_(noise_level).float()  # torch.full((1, img_L.size(1), img_L.size(2)), noise_level)
  
              # ---------------------------------
              # add noise
              # ---------------------------------
              noise = torch.randn(img_L.size()).mul_(noise_level).float()
              img_L.add_(noise)
  
          else:
              """
              # --------------------------------
              # get L/H/M image pairs
              # --------------------------------
              """
              img_H = util.uint2single(img_H)
              img_L = np.copy(img_H)
              np.random.seed(seed=0)
              img_L += np.random.normal(0, self.sigma_test/255.0, img_L.shape)
              noise_level_map = torch.ones((1, img_L.shape[0], img_L.shape[1])).mul_(self.sigma_test/255.0).float()  # torch.full((1, img_L.size(1), img_L.size(2)), noise_level)
  
              # ---------------------------------
              # L/H image pairs
              # ---------------------------------
              img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
  
          """
          # -------------------------------------
          # concat L and noise level map M
          # -------------------------------------
          """
          img_L = torch.cat((img_L, noise_level_map), 0)
  
          return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path}
  
      def __len__(self):
          return len(self.paths_H)
  