text: 
  from __future__ import absolute_import, division, print_function
  
  import os
  import random
  import numpy as np
  import copy
  from PIL import Image  # using pillow-simd for increased speed
  
  import torch
  import torch.utils.data as data
  from torchvision import transforms
  
  
  def pil_loader(path):
      # open path as file to avoid ResourceWarning
      # (https://github.com/python-pillow/Pillow/issues/835)
      with open(path, 'rb') as f:
          with Image.open(f) as img:
              return img.convert('RGB')
  
  
  class MonoDataset(data.Dataset):
      """Superclass for monocular dataloaders
      Args:
          data_path
          filenames
          height
          width
          frame_idxs
          num_scales
          is_train
          img_ext
      """
      def __init__(self,
                   data_path,
                   filenames,
                   height,
                   width,
                   frame_sides,
                   num_scales,
                   is_train=False,
                   img_ext='.png'):
          super(MonoDataset, self).__init__()
  
          self.data_path = data_path
          self.filenames = filenames#list , like '2011_09_26/2011_09_26_drive_0001_sync 1 l'
          self.height = height
          self.width = width
          self.num_scales = num_scales
          self.interp = Image.ANTIALIAS
  
          self.frame_sides = frame_sides
  
          self.is_train = is_train#unsuper train or evaluation
          self.img_ext = img_ext
  
          self.loader = pil_loader
          self.to_tensor = transforms.ToTensor()
  
          # We need to specify augmentations differently in newer versions of torchvision.
          # We first try the newer tuple version; if this fails we fall back to scalars
          try:
              self.brightness = (0.8, 1.2)
              self.contrast = (0.8, 1.2)
              self.saturation = (0.8, 1.2)
              self.hue = (-0.1, 0.1)
              transforms.ColorJitter.get_params(
                  self.brightness, self.contrast, self.saturation, self.hue)
          except TypeError:
              self.brightness = 0.2
              self.contrast = 0.2
              self.saturation = 0.2
              self.hue = 0.1
  
          self.resize = {}
          for i in range(self.num_scales):
              s = 2 ** i
              self.resize[i] = transforms.Resize((self.height // s, self.width // s),
                                                 interpolation=self.interp)
  
          self.load_depth = self.check_depth()
          #self.load_depth = False
  
  
      #private
      def preprocess(self, inputs, color_aug):
          """Resize colour images to the required scales and augment if required
          We create the color_aug object in advance and apply the same augmentation to all
          images in this item. This ensures that all images input to the pose network receive the
          same augmentation.
          """
          for k in list(inputs):
              frame = inputs[k]
              if "color" in k:
                  n, im, i = k
                  for i in range(self.num_scales):
                      inputs[(n, im, i)] = self.resize[i](inputs[(n, im, i - 1)])
  
          for k in list(inputs):
              f = inputs[k]
              if "color" in k:
                  n, im, i = k
                  inputs[(n, im, i)] = self.to_tensor(f)
                  inputs[(n + "_aug", im, i)] = self.to_tensor(color_aug(f))
  
      def __len__(self):
          return len(self.filenames)
  
      def __getitem__(self, index):
          """Returns a single training item(inputs ) from the dataset as a dictionary.
          Values correspond to torch tensors.
          Keys in the dictionary are either strings or tuples:
              ("color", <frame_id>, <scale>)          for raw colour images,
              ("color_aug", <frame_id>, <scale>)      for augmented colour images,
              ("K", scale) or ("inv_K", scale)        for camera intrinsics,
              "stereo_T"                              for camera extrinsics, and
              "depth_gt"                              for ground truth depth maps.
          <frame_id> is either:
              an integer (e.g. 0, -1, or 1) representing the temporal step relative to 'index',
          or
              "s" for the opposite image in the stereo pair.
          <scale> is an integer representing the scale of the image relative to the fullsize image:
              -1      images at native resolution as loaded from disk#1242x362
              0       images resized to (self.width,      self.height     )#640x192
              1       images resized to (self.width // 2, self.height // 2)
              2       images resized to (self.width // 4, self.height // 4)
              3       images resized to (self.width // 8, self.height // 8)
          """
          inputs = {}
  
          do_color_aug = self.is_train and random.random() > 0.5
          do_flip = self.is_train and random.random() > 0.5
  
          line = self.filenames[index].split()
          folder = line[0]#pic_path
  
  
          if len(line) == 3 or len(line)==2:# 2 for monocular files struct
              frame_index = int(line[1])
          else:
              frame_index = 0
  
  
  
          if len(line) == 3:
              side = line[2]
          else:
              side = None
  
  
  
          for i in self.frame_sides:
              inputs[("color", i, -1)] = self.get_color(folder, frame_index + i, side, do_flip)#inputs得到scale == -1的前 中后三帧
  
  
  
          # adjusting intrinsics to match each scale in the pyramid
          for scale in range(self.num_scales):
              K = self.K.copy()
  
              K[0, :] *= self.width // (2 ** scale)
              K[1, :] *= self.height // (2 ** scale)
  
              inv_K = np.linalg.pinv(K)
  
              inputs[("K", scale)] = torch.from_numpy(K)
              inputs[("inv_K", scale)] = torch.from_numpy(inv_K)
  
  
  
          if do_color_aug:
              color_aug = transforms.ColorJitter.get_params(#对图像进行稍微处理，aug 但是要保证深度一致
                  self.brightness, self.contrast, self.saturation, self.hue)
          else:
              color_aug = (lambda x: x)
  
          self.preprocess(inputs, color_aug)#scalse,aug generate to 38
  
  
  
          for i in self.frame_sides:
              del inputs[("color", i, -1)]
              del inputs[("color_aug", i, -1)]
  
  
  
          if self.load_depth:
              depth_gt = self.get_depth(folder, frame_index, side, do_flip)
              inputs["depth_gt"] = np.expand_dims(depth_gt, 0)
              inputs["depth_gt"] = torch.from_numpy(inputs["depth_gt"].astype(np.float32))
  
  
  
  
  
          return inputs
  
  
      #多态
      def get_color(self, folder, frame_index, side, do_flip):
          raise NotImplementedError
  
      def check_depth(self):
          raise NotImplementedError
  
      def get_depth(self, folder, frame_index, side, do_flip):
          raise NotImplementedError