repo_description: Convolutional recurrent network in pytorch
repo_topics: neural-network recognition scene-texts
code: #!/usr/bin/python
  # encoding: utf-8
  
  import torch
  import torch.nn as nn
  from torch.autograd import Variable
  import collections
  
  
  class strLabelConverter(object):
      """Convert between str and label.
  
      NOTE:
          Insert `blank` to the alphabet for CTC.
  
      Args:
          alphabet (str): set of the possible characters.
          ignore_case (bool, default=True): whether or not to ignore all of the case.
      """
  
      def __init__(self, alphabet, ignore_case=True):
          self._ignore_case = ignore_case
          if self._ignore_case:
              alphabet = alphabet.lower()
          self.alphabet = alphabet + '-'  # for `-1` index
  
          self.dict = {}
          for i, char in enumerate(alphabet):
              # NOTE: 0 is reserved for 'blank' required by wrap_ctc
              self.dict[char] = i + 1
  
      def encode(self, text):
          """Support batch or single str.
  
          Args:
              text (str or list of str): texts to convert.
  
          Returns:
              torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.
              torch.IntTensor [n]: length of each text.
          """
          if isinstance(text, str):
              text = [
                  self.dict[char.lower() if self._ignore_case else char]
                  for char in text
              ]
              length = [len(text)]
          elif isinstance(text, collections.Iterable):
              length = [len(s) for s in text]
              text = ''.join(text)
              text, _ = self.encode(text)
          return (torch.IntTensor(text), torch.IntTensor(length))
  
      def decode(self, t, length, raw=False):
          """Decode encoded texts back into strs.
  
          Args:
              torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.
              torch.IntTensor [n]: length of each text.
  
          Raises:
              AssertionError: when the texts and its length does not match.
  
          Returns:
              text (str or list of str): texts to convert.
          """
          if length.numel() == 1:
              length = length[0]
              assert t.numel() == length, "text with length: {} does not match declared length: {}".format(t.numel(), length)
              if raw:
                  return ''.join([self.alphabet[i - 1] for i in t])
              else:
                  char_list = []
                  for i in range(length):
                      if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])):
                          char_list.append(self.alphabet[t[i] - 1])
                  return ''.join(char_list)
          else:
              # batch mode
              assert t.numel() == length.sum(), "texts with length: {} does not match declared length: {}".format(t.numel(), length.sum())
              texts = []
              index = 0
              for i in range(length.numel()):
                  l = length[i]
                  texts.append(
                      self.decode(
                          t[index:index + l], torch.IntTensor([l]), raw=raw))
                  index += l
              return texts
  
  
  class averager(object):
      """Compute average for `torch.Variable` and `torch.Tensor`. """
  
      def __init__(self):
          self.reset()
  
      def add(self, v):
          if isinstance(v, Variable):
              count = v.data.numel()
              v = v.data.sum()
          elif isinstance(v, torch.Tensor):
              count = v.numel()
              v = v.sum()
  
          self.n_count += count
          self.sum += v
  
      def reset(self):
          self.n_count = 0
          self.sum = 0
  
      def val(self):
          res = 0
          if self.n_count != 0:
              res = self.sum / float(self.n_count)
          return res
  
  
  def oneHot(v, v_length, nc):
      batchSize = v_length.size(0)
      maxLength = v_length.max()
      v_onehot = torch.FloatTensor(batchSize, maxLength, nc).fill_(0)
      acc = 0
      for i in range(batchSize):
          length = v_length[i]
          label = v[acc:acc + length].view(-1, 1).long()
          v_onehot[i, :length].scatter_(1, label, 1.0)
          acc += length
      return v_onehot
  
  
  def loadData(v, data):
      v.data.resize_(data.size()).copy_(data)
  
  
  def prettyPrint(v):
      print('Size {0}, Type: {1}'.format(str(v.size()), v.data.type()))
      print('| Max: %f | Min: %f | Mean: %f' % (v.max().data[0], v.min().data[0],
                                                v.mean().data[0]))
  
  
  def assureRatio(img):
      """Ensure imgH <= imgW."""
      b, c, h, w = img.size()
      if h > w:
          main = nn.UpsamplingBilinear2d(size=(h, h), scale_factor=None)
          img = main(img)
      return img
  