text: import numpy as np
  
  
  class SimpleTransformer:
  
      """
      SimpleTransformer is a simple class for preprocessing and deprocessing
      images for caffe.
      """
  
      def __init__(self, mean=[128, 128, 128]):
          self.mean = np.array(mean, dtype=np.float32)
          self.scale = 1.0
  
      def set_mean(self, mean):
          """
          Set the mean to subtract for centering the data.
          """
          self.mean = mean
  
      def set_scale(self, scale):
          """
          Set the data scaling.
          """
          self.scale = scale
  
      def preprocess(self, im):
          """
          preprocess() emulate the pre-processing occurring in the vgg16 caffe
          prototxt.
          """
  
          im = np.float32(im)
          im = im[:, :, ::-1]  # change to BGR
          im -= self.mean
          im *= self.scale
          im = im.transpose((2, 0, 1))
  
          return im
  
      def deprocess(self, im):
          """
          inverse of preprocess()
          """
          im = im.transpose(1, 2, 0)
          im /= self.scale
          im += self.mean
          im = im[:, :, ::-1]  # change to RGB
  
          return np.uint8(im)
  
  
  class CaffeSolver:
  
      """
      Caffesolver is a class for creating a zf_solver.prototxt file. It sets default
      values and can export a solver parameter file.
      Note that all parameters are stored as strings. Strings variables are
      stored as strings in strings.
      """
  
      def __init__(self, testnet_prototxt_path="testnet.prototxt",
                   trainnet_prototxt_path="trainnet.prototxt", debug=False):
  
          self.sp = {}
  
          # critical:
          self.sp['base_lr'] = '0.001'
          self.sp['momentum'] = '0.9'
  
          # speed:
          self.sp['test_iter'] = '100'
          self.sp['test_interval'] = '250'
  
          # looks:
          self.sp['display'] = '25'
          self.sp['snapshot'] = '2500'
          self.sp['snapshot_prefix'] = '"snapshot"'  # string within a string!
  
          # learning rate policy
          self.sp['lr_policy'] = '"fixed"'
  
          # important, but rare:
          self.sp['gamma'] = '0.1'
          self.sp['weight_decay'] = '0.0005'
          self.sp['train_net'] = '"' + trainnet_prototxt_path + '"'
          self.sp['test_net'] = '"' + testnet_prototxt_path + '"'
  
          # pretty much never change these.
          self.sp['max_iter'] = '100000'
          self.sp['test_initialization'] = 'false'
          self.sp['average_loss'] = '25'  # this has to do with the display.
          self.sp['iter_size'] = '1'  # this is for accumulating gradients
  
          if (debug):
              self.sp['max_iter'] = '12'
              self.sp['test_iter'] = '1'
              self.sp['test_interval'] = '4'
              self.sp['display'] = '1'
  
      def add_from_file(self, filepath):
          """
          Reads a caffe solver prototxt file and updates the Caffesolver
          instance parameters.
          """
          with open(filepath, 'r') as f:
              for line in f:
                  if line[0] == '#':
                      continue
                  splitLine = line.split(':')
                  self.sp[splitLine[0].strip()] = splitLine[1].strip()
  
      def write(self, filepath):
          """
          Export solver parameters to INPUT "filepath". Sorted alphabetically.
          """
          f = open(filepath, 'w')
          for key, value in sorted(self.sp.items()):
              if not(type(value) is str):
                  raise TypeError('All solver parameters must be strings')
              f.write('%s: %s\n' % (key, value))
  