code: #! /usr/bin/python
  # -*- coding: utf-8 -*-
  
  import os
  import sys
  
  import gzip
  import math
  import pickle
  import progressbar
  import re
  import requests
  import shutil
  import tarfile
  import time
  import zipfile
  
  from tqdm import tqdm
  
  from six.moves import cPickle
  # from six.moves import zip
  
  from lxml import etree
  import xml.etree.ElementTree as ET
  
  if sys.version_info[0] == 2:
      from urllib import urlretrieve
  else:
      from urllib.request import urlretrieve
  
  import matplotlib.pyplot as plt
  
  import scipy.io as sio
  import numpy as np
  
  import tensorflow as tf
  from tensorflow.python.platform import gfile
  
  from tensorlayer import tl_logging as logging
  
  from tensorlayer import nlp
  from tensorlayer import utils
  from tensorlayer import visualize
  
  __all__ = [
      'assign_params',
      'del_file',
      'del_folder',
      'download_file_from_google_drive',
      'exists_or_mkdir',
      'file_exists',
      'folder_exists',
      'load_and_assign_npz',
      'load_and_assign_npz_dict',
      'load_ckpt',
      'load_cropped_svhn',
      'load_file_list',
      'load_folder_list',
      'load_npy_to_any',
      'load_npz',
      'maybe_download_and_extract',
      'natural_keys',
      'npz_to_W_pdf',
      'read_file',
      'save_any_to_npy',
      'save_ckpt',
      'save_npz',
      'save_npz_dict',
  ]
  
  
  ## Load dataset functions
  def load_mnist_dataset(shape=(-1, 784), path='data'):
      """Load the original mnist.
  
      Automatically download MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 digit images respectively.
  
      Parameters
      ----------
      shape : tuple
          The shape of digit images (the default is (-1, 784), alternatively (-1, 28, 28, 1)).
      path : str
          The path that the data is downloaded to.
  
      Returns
      -------
      X_train, y_train, X_val, y_val, X_test, y_test: tuple
          Return splitted training/validation/test set respectively.
  
      Examples
      --------
      >>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1,784), path='datasets')
      >>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
      """
      return _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/')
  
  
  def load_fashion_mnist_dataset(shape=(-1, 784), path='data'):
      """Load the fashion mnist.
  
      Automatically download fashion-MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 fashion images respectively, `examples <http://marubon-ds.blogspot.co.uk/2017/09/fashion-mnist-exploring.html>`__.
  
      Parameters
      ----------
      shape : tuple
          The shape of digit images (the default is (-1, 784), alternatively (-1, 28, 28, 1)).
      path : str
          The path that the data is downloaded to.
  
      Returns
      -------
      X_train, y_train, X_val, y_val, X_test, y_test: tuple
          Return splitted training/validation/test set respectively.
  
      Examples
      --------
      >>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_fashion_mnist_dataset(shape=(-1,784), path='datasets')
      >>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_fashion_mnist_dataset(shape=(-1, 28, 28, 1))
      """
      return _load_mnist_dataset(
          shape, path, name='fashion_mnist', url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'
      )
  
  
  def _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/'):
      """A generic function to load mnist-like dataset.
  
      Parameters:
      ----------
      shape : tuple
          The shape of digit images.
      path : str
          The path that the data is downloaded to.
      name : str
          The dataset name you want to use(the default is 'mnist').
      url : str
          The url of dataset(the default is 'http://yann.lecun.com/exdb/mnist/').
      """
      path = os.path.join(path, name)
  
      # Define functions for loading mnist-like data's images and labels.
      # For convenience, they also download the requested files if needed.
      def load_mnist_images(path, filename):
          filepath = maybe_download_and_extract(filename, path, url)
  
          logging.info(filepath)
          # Read the inputs in Yann LeCun's binary format.
          with gzip.open(filepath, 'rb') as f:
              data = np.frombuffer(f.read(), np.uint8, offset=16)
          # The inputs are vectors now, we reshape them to monochrome 2D images,
          # following the shape convention: (examples, channels, rows, columns)
          data = data.reshape(shape)
          # The inputs come as bytes, we convert them to float32 in range [0,1].
          # (Actually to range [0, 255/256], for compatibility to the version
          # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)
          return data / np.float32(256)
  
      def load_mnist_labels(path, filename):
          filepath = maybe_download_and_extract(filename, path, url)
          # Read the labels in Yann LeCun's binary format.
          with gzip.open(filepath, 'rb') as f:
              data = np.frombuffer(f.read(), np.uint8, offset=8)
          # The labels are vectors of integers now, that's exactly what we want.
          return data
  
      # Download and read the training and test set images and labels.
      logging.info("Load or Download {0} > {1}".format(name.upper(), path))
      X_train = load_mnist_images(path, 'train-images-idx3-ubyte.gz')
      y_train = load_mnist_labels(path, 'train-labels-idx1-ubyte.gz')
      X_test = load_mnist_images(path, 't10k-images-idx3-ubyte.gz')
      y_test = load_mnist_labels(path, 't10k-labels-idx1-ubyte.gz')
  
      # We reserve the last 10000 training examples for validation.
      X_train, X_val = X_train[:-10000], X_train[-10000:]
      y_train, y_val = y_train[:-10000], y_train[-10000:]
  
      # We just return all the arrays in order, as expected in main().
      # (It doesn't matter how we do this as long as we can read them again.)
      X_train = np.asarray(X_train, dtype=np.float32)
      y_train = np.asarray(y_train, dtype=np.int32)
      X_val = np.asarray(X_val, dtype=np.float32)
      y_val = np.asarray(y_val, dtype=np.int32)
      X_test = np.asarray(X_test, dtype=np.float32)
      y_test = np.asarray(y_test, dtype=np.int32)
      return X_train, y_train, X_val, y_val, X_test, y_test
  
  
  def load_cifar10_dataset(shape=(-1, 32, 32, 3), path='data', plotable=False):
      """Load CIFAR-10 dataset.
  
      It consists of 60000 32x32 colour images in 10 classes, with
      6000 images per class. There are 50000 training images and 10000 test images.
  
      The dataset is divided into five training batches and one test batch, each with
      10000 images. The test batch contains exactly 1000 randomly-selected images from
      each class. The training batches contain the remaining images in random order,
      but some training batches may contain more images from one class than another.
      Between them, the training batches contain exactly 5000 images from each class.
  
      Parameters
      ----------
      shape : tupe
          The shape of digit images e.g. (-1, 3, 32, 32) and (-1, 32, 32, 3).
      path : str
          The path that the data is downloaded to, defaults is ``data/cifar10/``.
      plotable : boolean
          Whether to plot some image examples, False as default.
  
      Examples
      --------
      >>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3))
  
      References
      ----------
      - `CIFAR website <https://www.cs.toronto.edu/~kriz/cifar.html>`__
      - `Data download link <https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz>`__
      - `<https://teratail.com/questions/28932>`__
  
      """
      path = os.path.join(path, 'cifar10')
      logging.info("Load or Download cifar10 > {}".format(path))
  
      #Helper function to unpickle the data
      def unpickle(file):
          fp = open(file, 'rb')
          if sys.version_info.major == 2:
              data = pickle.load(fp)
          elif sys.version_info.major == 3:
              data = pickle.load(fp, encoding='latin-1')
          fp.close()
          return data
  
      filename = 'cifar-10-python.tar.gz'
      url = 'https://www.cs.toronto.edu/~kriz/'
      #Download and uncompress file
      maybe_download_and_extract(filename, path, url, extract=True)
  
      #Unpickle file and fill in data
      X_train = None
      y_train = []
      for i in range(1, 6):
          data_dic = unpickle(os.path.join(path, 'cifar-10-batches-py/', "data_batch_{}".format(i)))
          if i == 1:
              X_train = data_dic['data']
          else:
              X_train = np.vstack((X_train, data_dic['data']))
          y_train += data_dic['labels']
  
      test_data_dic = unpickle(os.path.join(path, 'cifar-10-batches-py/', "test_batch"))
      X_test = test_data_dic['data']
      y_test = np.array(test_data_dic['labels'])
  
      if shape == (-1, 3, 32, 32):
          X_test = X_test.reshape(shape)
          X_train = X_train.reshape(shape)
      elif shape == (-1, 32, 32, 3):
          X_test = X_test.reshape(shape, order='F')
          X_train = X_train.reshape(shape, order='F')
          X_test = np.transpose(X_test, (0, 2, 1, 3))
          X_train = np.transpose(X_train, (0, 2, 1, 3))
      else:
          X_test = X_test.reshape(shape)
          X_train = X_train.reshape(shape)
  
      y_train = np.array(y_train)
  
      if plotable:
          logging.info('\nCIFAR-10')
          fig = plt.figure(1)
  
          logging.info('Shape of a training image: X_train[0] %s' % X_train[0].shape)
  
          plt.ion()  # interactive mode
          count = 1
          for _ in range(10):  # each row
              for _ in range(10):  # each column
                  _ = fig.add_subplot(10, 10, count)
                  if shape == (-1, 3, 32, 32):
                      # plt.imshow(X_train[count-1], interpolation='nearest')
                      plt.imshow(np.transpose(X_train[count - 1], (1, 2, 0)), interpolation='nearest')
                      # plt.imshow(np.transpose(X_train[count-1], (2, 1, 0)), interpolation='nearest')
                  elif shape == (-1, 32, 32, 3):
                      plt.imshow(X_train[count - 1], interpolation='nearest')
                      # plt.imshow(np.transpose(X_train[count-1], (1, 0, 2)), interpolation='nearest')
                  else:
                      raise Exception("Do not support the given 'shape' to plot the image examples")
                  plt.gca().xaxis.set_major_locator(plt.NullLocator())  # 不显示刻度(tick)
                  plt.gca().yaxis.set_major_locator(plt.NullLocator())
                  count = count + 1
          plt.draw()  # interactive mode
          plt.pause(3)  # interactive mode
  
          logging.info("X_train: %s" % X_train.shape)
          logging.info("y_train: %s" % y_train.shape)
          logging.info("X_test:  %s" % X_test.shape)
          logging.info("y_test:  %s" % y_test.shape)
  
      X_train = np.asarray(X_train, dtype=np.float32)
      X_test = np.asarray(X_test, dtype=np.float32)
      y_train = np.asarray(y_train, dtype=np.int32)
      y_test = np.asarray(y_test, dtype=np.int32)
  
      return X_train, y_train, X_test, y_test
  
  
  def load_cropped_svhn(path='data', include_extra=True):
      """Load Cropped SVHN.
  
      The Cropped Street View House Numbers (SVHN) Dataset contains 32x32x3 RGB images.
      Digit '1' has label 1, '9' has label 9 and '0' has label 0 (the original dataset uses 10 to represent '0'), see `ufldl website <http://ufldl.stanford.edu/housenumbers/>`__.
  
      Parameters
      ----------
      path : str
          The path that the data is downloaded to.
      include_extra : boolean
          If True (default), add extra images to the training set.
  
      Returns
      -------
      X_train, y_train, X_test, y_test: tuple
          Return splitted training/test set respectively.
  
      Examples
      ---------
      >>> X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
      >>> tl.vis.save_images(X_train[0:100], [10, 10], 'svhn.png')
  
      """
  
      start_time = time.time()
  
      path = os.path.join(path, 'cropped_svhn')
      logging.info("Load or Download Cropped SVHN > {} | include extra images: {}".format(path, include_extra))
      url = "http://ufldl.stanford.edu/housenumbers/"
  
      np_file = os.path.join(path, "train_32x32.npz")
      if file_exists(np_file) is False:
          filename = "train_32x32.mat"
          filepath = maybe_download_and_extract(filename, path, url)
          mat = sio.loadmat(filepath)
          X_train = mat['X'] / 255.0  # to [0, 1]
          X_train = np.transpose(X_train, (3, 0, 1, 2))
          y_train = np.squeeze(mat['y'], axis=1)
          y_train[y_train == 10] = 0  # replace 10 to 0
          np.savez(np_file, X=X_train, y=y_train)
          del_file(filepath)
      else:
          v = np.load(np_file)
          X_train = v['X']
          y_train = v['y']
      logging.info("  n_train: {}".format(len(y_train)))
  
      np_file = os.path.join(path, "test_32x32.npz")
      if file_exists(np_file) is False:
          filename = "test_32x32.mat"
          filepath = maybe_download_and_extract(filename, path, url)
          mat = sio.loadmat(filepath)
          X_test = mat['X'] / 255.0
          X_test = np.transpose(X_test, (3, 0, 1, 2))
          y_test = np.squeeze(mat['y'], axis=1)
          y_test[y_test == 10] = 0
          np.savez(np_file, X=X_test, y=y_test)
          del_file(filepath)
      else:
          v = np.load(np_file)
          X_test = v['X']
          y_test = v['y']
      logging.info("  n_test: {}".format(len(y_test)))
  
      if include_extra:
          logging.info("  getting extra 531131 images, please wait ...")
          np_file = os.path.join(path, "extra_32x32.npz")
          if file_exists(np_file) is False:
              logging.info("  the first time to load extra images will take long time to convert the file format ...")
              filename = "extra_32x32.mat"
              filepath = maybe_download_and_extract(filename, path, url)
              mat = sio.loadmat(filepath)
              X_extra = mat['X'] / 255.0
              X_extra = np.transpose(X_extra, (3, 0, 1, 2))
              y_extra = np.squeeze(mat['y'], axis=1)
              y_extra[y_extra == 10] = 0
              np.savez(np_file, X=X_extra, y=y_extra)
              del_file(filepath)
          else:
              v = np.load(np_file)
              X_extra = v['X']
              y_extra = v['y']
          # print(X_train.shape, X_extra.shape)
          logging.info("  adding n_extra {} to n_train {}".format(len(y_extra), len(y_train)))
          t = time.time()
          X_train = np.concatenate((X_train, X_extra), 0)
          y_train = np.concatenate((y_train, y_extra), 0)
          # X_train = np.append(X_train, X_extra, axis=0)
          # y_train = np.append(y_train, y_extra, axis=0)
          logging.info("  added n_extra {} to n_train {} took {}s".format(len(y_extra), len(y_train), time.time() - t))
      else:
          logging.info("  no extra images are included")
      logging.info("  image size: %s n_train: %d n_test: %d" % (str(X_train.shape[1:4]), len(y_train), len(y_test)))
      logging.info("  took: {}s".format(int(time.time() - start_time)))
      return X_train, y_train, X_test, y_test
  
  
  def load_ptb_dataset(path='data'):
      """Load Penn TreeBank (PTB) dataset.
  
      It is used in many LANGUAGE MODELING papers,
      including "Empirical Evaluation and Combination of Advanced Language
      Modeling Techniques", "Recurrent Neural Network Regularization".
      It consists of 929k training words, 73k validation words, and 82k test
      words. It has 10k words in its vocabulary.
  
      Parameters
      ----------
      path : str
          The path that the data is downloaded to, defaults is ``data/ptb/``.
  
      Returns
      --------
      train_data, valid_data, test_data : list of int
          The training, validating and testing data in integer format.
      vocab_size : int
          The vocabulary size.
  
      Examples
      --------
      >>> train_data, valid_data, test_data, vocab_size = tl.files.load_ptb_dataset()
  
      References
      ---------------
      - ``tensorflow.models.rnn.ptb import reader``
      - `Manual download <http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz>`__
  
      Notes
      ------
      - If you want to get the raw data, see the source code.
  
      """
      path = os.path.join(path, 'ptb')
      logging.info("Load or Download Penn TreeBank (PTB) dataset > {}".format(path))
  
      #Maybe dowload and uncompress tar, or load exsisting files
      filename = 'simple-examples.tgz'
      url = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/'
      maybe_download_and_extract(filename, path, url, extract=True)
  
      data_path = os.path.join(path, 'simple-examples', 'data')
      train_path = os.path.join(data_path, "ptb.train.txt")
      valid_path = os.path.join(data_path, "ptb.valid.txt")
      test_path = os.path.join(data_path, "ptb.test.txt")
  
      word_to_id = nlp.build_vocab(nlp.read_words(train_path))
  
      train_data = nlp.words_to_word_ids(nlp.read_words(train_path), word_to_id)
      valid_data = nlp.words_to_word_ids(nlp.read_words(valid_path), word_to_id)
      test_data = nlp.words_to_word_ids(nlp.read_words(test_path), word_to_id)
      vocab_size = len(word_to_id)
  
      # logging.info(nlp.read_words(train_path)) # ... 'according', 'to', 'mr.', '<unk>', '<eos>']
      # logging.info(train_data)                 # ...  214,         5,    23,    1,       2]
      # logging.info(word_to_id)                 # ... 'beyond': 1295, 'anti-nuclear': 9599, 'trouble': 1520, '<eos>': 2 ... }
      # logging.info(vocabulary)                 # 10000
      # exit()
      return train_data, valid_data, test_data, vocab_size
  
  
  def load_matt_mahoney_text8_dataset(path='data'):
      """Load Matt Mahoney's dataset.
  
      Download a text file from Matt Mahoney's website
      if not present, and make sure it's the right size.
      Extract the first file enclosed in a zip file as a list of words.
      This dataset can be used for Word Embedding.
  
      Parameters
      ----------
      path : str
          The path that the data is downloaded to, defaults is ``data/mm_test8/``.
  
      Returns
      --------
      list of str
          The raw text data e.g. [.... 'their', 'families', 'who', 'were', 'expelled', 'from', 'jerusalem', ...]
  
      Examples
      --------
      >>> words = tl.files.load_matt_mahoney_text8_dataset()
      >>> print('Data size', len(words))
  
      """
      path = os.path.join(path, 'mm_test8')
      logging.info("Load or Download matt_mahoney_text8 Dataset> {}".format(path))
  
      filename = 'text8.zip'
      url = 'http://mattmahoney.net/dc/'
      maybe_download_and_extract(filename, path, url, expected_bytes=31344016)
  
      with zipfile.ZipFile(os.path.join(path, filename)) as f:
          word_list = f.read(f.namelist()[0]).split()
          for idx, _ in enumerate(word_list):
              word_list[idx] = word_list[idx].decode()
      return word_list
  
  
  def load_imdb_dataset(
          path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
          index_from=3
  ):
      """Load IMDB dataset.
  
      Parameters
      ----------
      path : str
          The path that the data is downloaded to, defaults is ``data/imdb/``.
      nb_words : int
          Number of words to get.
      skip_top : int
          Top most frequent words to ignore (they will appear as oov_char value in the sequence data).
      maxlen : int
          Maximum sequence length. Any longer sequence will be truncated.
      seed : int
          Seed for reproducible data shuffling.
      start_char : int
          The start of a sequence will be marked with this character. Set to 1 because 0 is usually the padding character.
      oov_char : int
          Words that were cut out because of the num_words or skip_top limit will be replaced with this character.
      index_from : int
          Index actual words with this index and higher.
  
      Examples
      --------
      >>> X_train, y_train, X_test, y_test = tl.files.load_imdb_dataset(
      ...                                 nb_words=20000, test_split=0.2)
      >>> print('X_train.shape', X_train.shape)
      (20000,)  [[1, 62, 74, ... 1033, 507, 27],[1, 60, 33, ... 13, 1053, 7]..]
      >>> print('y_train.shape', y_train.shape)
      (20000,)  [1 0 0 ..., 1 0 1]
  
      References
      -----------
      - `Modified from keras. <https://github.com/fchollet/keras/blob/master/keras/datasets/imdb.py>`__
  
      """
      path = os.path.join(path, 'imdb')
  
      filename = "imdb.pkl"
      url = 'https://s3.amazonaws.com/text-datasets/'
      maybe_download_and_extract(filename, path, url)
  
      if filename.endswith(".gz"):
          f = gzip.open(os.path.join(path, filename), 'rb')
      else:
          f = open(os.path.join(path, filename), 'rb')
  
      X, labels = cPickle.load(f)
      f.close()
  
      np.random.seed(seed)
      np.random.shuffle(X)
      np.random.seed(seed)
      np.random.shuffle(labels)
  
      if start_char is not None:
          X = [[start_char] + [w + index_from for w in x] for x in X]
      elif index_from:
          X = [[w + index_from for w in x] for x in X]
  
      if maxlen:
          new_X = []
          new_labels = []
          for x, y in zip(X, labels):
              if len(x) < maxlen:
                  new_X.append(x)
                  new_labels.append(y)
          X = new_X
          labels = new_labels
      if not X:
          raise Exception(
              'After filtering for sequences shorter than maxlen=' + str(maxlen) + ', no sequence was kept. '
              'Increase maxlen.'
          )
      if not nb_words:
          nb_words = max([max(x) for x in X])
  
      # by convention, use 2 as OOV word
      # reserve 'index_from' (=3 by default) characters: 0 (padding), 1 (start), 2 (OOV)
      if oov_char is not None:
          X = [[oov_char if (w >= nb_words or w < skip_top) else w for w in x] for x in X]
      else:
          nX = []
          for x in X:
              nx = []
              for w in x:
                  if (w >= nb_words or w < skip_top):
                      nx.append(w)
              nX.append(nx)
          X = nX
  
      X_train = np.array(X[:int(len(X) * (1 - test_split))])
      y_train = np.array(labels[:int(len(X) * (1 - test_split))])
  
      X_test = np.array(X[int(len(X) * (1 - test_split)):])
      y_test = np.array(labels[int(len(X) * (1 - test_split)):])
  
      return X_train, y_train, X_test, y_test
  
  
  def load_nietzsche_dataset(path='data'):
      """Load Nietzsche dataset.
  
      Parameters
      ----------
      path : str
          The path that the data is downloaded to, defaults is ``data/nietzsche/``.
  
      Returns
      --------
      str
          The content.
  
      Examples
      --------
      >>> see tutorial_generate_text.py
      >>> words = tl.files.load_nietzsche_dataset()
      >>> words = basic_clean_str(words)
      >>> words = words.split()
  
      """
      logging.info("Load or Download nietzsche dataset > {}".format(path))
      path = os.path.join(path, 'nietzsche')
  
      filename = "nietzsche.txt"
      url = 'https://s3.amazonaws.com/text-datasets/'
      filepath = maybe_download_and_extract(filename, path, url)
  
      with open(filepath, "r") as f:
          words = f.read()
          return words
  
  
  def load_wmt_en_fr_dataset(path='data'):
      """Load WMT'15 English-to-French translation dataset.
  
      It will download the data from the WMT'15 Website (10^9-French-English corpus), and the 2013 news test from the same site as development set.
      Returns the directories of training data and test data.
  
      Parameters
      ----------
      path : str
          The path that the data is downloaded to, defaults is ``data/wmt_en_fr/``.
  
      References
      ----------
      - Code modified from /tensorflow/models/rnn/translation/data_utils.py
  
      Notes
      -----
      Usually, it will take a long time to download this dataset.
  
      """
      path = os.path.join(path, 'wmt_en_fr')
      # URLs for WMT data.
      _WMT_ENFR_TRAIN_URL = "http://www.statmt.org/wmt10/"
      _WMT_ENFR_DEV_URL = "http://www.statmt.org/wmt15/"
  
      def gunzip_file(gz_path, new_path):
          """Unzips from gz_path into new_path."""
          logging.info("Unpacking %s to %s" % (gz_path, new_path))
          with gzip.open(gz_path, "rb") as gz_file:
              with open(new_path, "wb") as new_file:
                  for line in gz_file:
                      new_file.write(line)
  
      def get_wmt_enfr_train_set(path):
          """Download the WMT en-fr training corpus to directory unless it's there."""
          filename = "training-giga-fren.tar"
          maybe_download_and_extract(filename, path, _WMT_ENFR_TRAIN_URL, extract=True)
          train_path = os.path.join(path, "giga-fren.release2.fixed")
          gunzip_file(train_path + ".fr.gz", train_path + ".fr")
          gunzip_file(train_path + ".en.gz", train_path + ".en")
          return train_path
  
      def get_wmt_enfr_dev_set(path):
          """Download the WMT en-fr training corpus to directory unless it's there."""
          filename = "dev-v2.tgz"
          dev_file = maybe_download_and_extract(filename, path, _WMT_ENFR_DEV_URL, extract=False)
          dev_name = "newstest2013"
          dev_path = os.path.join(path, "newstest2013")
          if not (gfile.Exists(dev_path + ".fr") and gfile.Exists(dev_path + ".en")):
              logging.info("Extracting tgz file %s" % dev_file)
              with tarfile.open(dev_file, "r:gz") as dev_tar:
                  fr_dev_file = dev_tar.getmember("dev/" + dev_name + ".fr")
                  en_dev_file = dev_tar.getmember("dev/" + dev_name + ".en")
                  fr_dev_file.name = dev_name + ".fr"  # Extract without "dev/" prefix.
                  en_dev_file.name = dev_name + ".en"
                  dev_tar.extract(fr_dev_file, path)
                  dev_tar.extract(en_dev_file, path)
          return dev_path
  
      logging.info("Load or Download WMT English-to-French translation > {}".format(path))
  
      train_path = get_wmt_enfr_train_set(path)
      dev_path = get_wmt_enfr_dev_set(path)
  
      return train_path, dev_path
  
  
  def load_flickr25k_dataset(tag='sky', path="data", n_threads=50, printable=False):
      """Load Flickr25K dataset.
  
      Returns a list of images by a given tag from Flick25k dataset,
      it will download Flickr25k from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__
      at the first time you use it.
  
      Parameters
      ------------
      tag : str or None
          What images to return.
              - If you want to get images with tag, use string like 'dog', 'red', see `Flickr Search <https://www.flickr.com/search/>`__.
              - If you want to get all images, set to ``None``.
  
      path : str
          The path that the data is downloaded to, defaults is ``data/flickr25k/``.
      n_threads : int
          The number of thread to read image.
      printable : boolean
          Whether to print infomation when reading images, default is ``False``.
  
      Examples
      -----------
      Get images with tag of sky
  
      >>> images = tl.files.load_flickr25k_dataset(tag='sky')
  
      Get all images
  
      >>> images = tl.files.load_flickr25k_dataset(tag=None, n_threads=100, printable=True)
  
      """
      path = os.path.join(path, 'flickr25k')
  
      filename = 'mirflickr25k.zip'
      url = 'http://press.liacs.nl/mirflickr/mirflickr25k/'
  
      # download dataset
      if folder_exists(os.path.join(path, "mirflickr")) is False:
          logging.info("[*] Flickr25k is nonexistent in {}".format(path))
          maybe_download_and_extract(filename, path, url, extract=True)
          del_file(os.path.join(path, filename))
  
      # return images by the given tag.
      # 1. image path list
      folder_imgs = os.path.join(path, "mirflickr")
      path_imgs = load_file_list(path=folder_imgs, regx='\\.jpg', printable=False)
      path_imgs.sort(key=natural_keys)
  
      # 2. tag path list
      folder_tags = os.path.join(path, "mirflickr", "meta", "tags")
      path_tags = load_file_list(path=folder_tags, regx='\\.txt', printable=False)
      path_tags.sort(key=natural_keys)
  
      # 3. select images
      if tag is None:
          logging.info("[Flickr25k] reading all images")
      else:
          logging.info("[Flickr25k] reading images with tag: {}".format(tag))
      images_list = []
      for idx, _v in enumerate(path_tags):
          tags = read_file(os.path.join(folder_tags, path_tags[idx])).split('\n')
          # logging.info(idx+1, tags)
          if tag is None or tag in tags:
              images_list.append(path_imgs[idx])
  
      images = visualize.read_images(images_list, folder_imgs, n_threads=n_threads, printable=printable)
      return images
  
  
  def load_flickr1M_dataset(tag='sky', size=10, path="data", n_threads=50, printable=False):
      """Load Flick1M dataset.
  
      Returns a list of images by a given tag from Flickr1M dataset,
      it will download Flickr1M from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__
      at the first time you use it.
  
      Parameters
      ------------
      tag : str or None
          What images to return.
              - If you want to get images with tag, use string like 'dog', 'red', see `Flickr Search <https://www.flickr.com/search/>`__.
              - If you want to get all images, set to ``None``.
  
      size : int
          integer between 1 to 10. 1 means 100k images ... 5 means 500k images, 10 means all 1 million images. Default is 10.
      path : str
          The path that the data is downloaded to, defaults is ``data/flickr25k/``.
      n_threads : int
          The number of thread to read image.
      printable : boolean
          Whether to print infomation when reading images, default is ``False``.
  
      Examples
      ----------
      Use 200k images
  
      >>> images = tl.files.load_flickr1M_dataset(tag='zebra', size=2)
  
      Use 1 Million images
  
      >>> images = tl.files.load_flickr1M_dataset(tag='zebra')
  
      """
  
      path = os.path.join(path, 'flickr1M')
      logging.info("[Flickr1M] using {}% of images = {}".format(size * 10, size * 100000))
      images_zip = [
          'images0.zip', 'images1.zip', 'images2.zip', 'images3.zip', 'images4.zip', 'images5.zip', 'images6.zip',
          'images7.zip', 'images8.zip', 'images9.zip'
      ]
      tag_zip = 'tags.zip'
      url = 'http://press.liacs.nl/mirflickr/mirflickr1m/'
  
      # download dataset
      for image_zip in images_zip[0:size]:
          image_folder = image_zip.split(".")[0]
          # logging.info(path+"/"+image_folder)
          if folder_exists(os.path.join(path, image_folder)) is False:
              # logging.info(image_zip)
              logging.info("[Flickr1M] {} is missing in {}".format(image_folder, path))
              maybe_download_and_extract(image_zip, path, url, extract=True)
              del_file(os.path.join(path, image_zip))
              # os.system("mv {} {}".format(os.path.join(path, 'images'), os.path.join(path, image_folder)))
              shutil.move(os.path.join(path, 'images'), os.path.join(path, image_folder))
          else:
              logging.info("[Flickr1M] {} exists in {}".format(image_folder, path))
  
      # download tag
      if folder_exists(os.path.join(path, "tags")) is False:
          logging.info("[Flickr1M] tag files is nonexistent in {}".format(path))
          maybe_download_and_extract(tag_zip, path, url, extract=True)
          del_file(os.path.join(path, tag_zip))
      else:
          logging.info("[Flickr1M] tags exists in {}".format(path))
  
      # 1. image path list
      images_list = []
      images_folder_list = []
      for i in range(0, size):
          images_folder_list += load_folder_list(path=os.path.join(path, 'images%d' % i))
      images_folder_list.sort(key=lambda s: int(s.split('/')[-1]))  # folder/images/ddd
  
      for folder in images_folder_list[0:size * 10]:
          tmp = load_file_list(path=folder, regx='\\.jpg', printable=False)
          tmp.sort(key=lambda s: int(s.split('.')[-2]))  # ddd.jpg
          images_list.extend([os.path.join(folder, x) for x in tmp])
  
      # 2. tag path list
      tag_list = []
      tag_folder_list = load_folder_list(os.path.join(path, "tags"))
  
      # tag_folder_list.sort(key=lambda s: int(s.split("/")[-1]))  # folder/images/ddd
      tag_folder_list.sort(key=lambda s: int(os.path.basename(s)))
  
      for folder in tag_folder_list[0:size * 10]:
          tmp = load_file_list(path=folder, regx='\\.txt', printable=False)
          tmp.sort(key=lambda s: int(s.split('.')[-2]))  # ddd.txt
          tmp = [os.path.join(folder, s) for s in tmp]
          tag_list += tmp
  
      # 3. select images
      logging.info("[Flickr1M] searching tag: {}".format(tag))
      select_images_list = []
      for idx, _val in enumerate(tag_list):
          tags = read_file(tag_list[idx]).split('\n')
          if tag in tags:
              select_images_list.append(images_list[idx])
  
      logging.info("[Flickr1M] reading images with tag: {}".format(tag))
      images = visualize.read_images(select_images_list, '', n_threads=n_threads, printable=printable)
      return images
  
  
  def load_cyclegan_dataset(filename='summer2winter_yosemite', path='data'):
      """Load images from CycleGAN's database, see `this link <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`__.
  
      Parameters
      ------------
      filename : str
          The dataset you want, see `this link <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`__.
      path : str
          The path that the data is downloaded to, defaults is `data/cyclegan`
  
      Examples
      ---------
      >>> im_train_A, im_train_B, im_test_A, im_test_B = load_cyclegan_dataset(filename='summer2winter_yosemite')
  
      """
      path = os.path.join(path, 'cyclegan')
      url = 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/'
  
      if folder_exists(os.path.join(path, filename)) is False:
          logging.info("[*] {} is nonexistent in {}".format(filename, path))
          maybe_download_and_extract(filename + '.zip', path, url, extract=True)
          del_file(os.path.join(path, filename + '.zip'))
  
      def load_image_from_folder(path):
          path_imgs = load_file_list(path=path, regx='\\.jpg', printable=False)
          return visualize.read_images(path_imgs, path=path, n_threads=10, printable=False)
  
      im_train_A = load_image_from_folder(os.path.join(path, filename, "trainA"))
      im_train_B = load_image_from_folder(os.path.join(path, filename, "trainB"))
      im_test_A = load_image_from_folder(os.path.join(path, filename, "testA"))
      im_test_B = load_image_from_folder(os.path.join(path, filename, "testB"))
  
      def if_2d_to_3d(images):  # [h, w] --> [h, w, 3]
          for i, _v in enumerate(images):
              if len(images[i].shape) == 2:
                  images[i] = images[i][:, :, np.newaxis]
                  images[i] = np.tile(images[i], (1, 1, 3))
          return images
  
      im_train_A = if_2d_to_3d(im_train_A)
      im_train_B = if_2d_to_3d(im_train_B)
      im_test_A = if_2d_to_3d(im_test_A)
      im_test_B = if_2d_to_3d(im_test_B)
  
      return im_train_A, im_train_B, im_test_A, im_test_B
  
  
  def download_file_from_google_drive(ID, destination):
      """Download file from Google Drive.
  
      See ``tl.files.load_celebA_dataset`` for example.
  
      Parameters
      --------------
      ID : str
          The driver ID.
      destination : str
          The destination for save file.
  
      """
  
      def save_response_content(response, destination, chunk_size=32 * 1024):
          total_size = int(response.headers.get('content-length', 0))
          with open(destination, "wb") as f:
              for chunk in tqdm(response.iter_content(chunk_size), total=total_size, unit='B', unit_scale=True,
                                desc=destination):
                  if chunk:  # filter out keep-alive new chunks
                      f.write(chunk)
  
      def get_confirm_token(response):
          for key, value in response.cookies.items():
              if key.startswith('download_warning'):
                  return value
          return None
  
      URL = "https://docs.google.com/uc?export=download"
      session = requests.Session()
  
      response = session.get(URL, params={'id': ID}, stream=True)
      token = get_confirm_token(response)
  
      if token:
          params = {'id': ID, 'confirm': token}
          response = session.get(URL, params=params, stream=True)
      save_response_content(response, destination)
  
  
  def load_celebA_dataset(path='data'):
      """Load CelebA dataset
  
      Return a list of image path.
  
      Parameters
      -----------
      path : str
          The path that the data is downloaded to, defaults is ``data/celebA/``.
  
      """
      data_dir = 'celebA'
      filename, drive_id = "img_align_celeba.zip", "0B7EVK8r0v71pZjFTYXZWM3FlRnM"
      save_path = os.path.join(path, filename)
      image_path = os.path.join(path, data_dir)
      if os.path.exists(image_path):
          logging.info('[*] {} already exists'.format(save_path))
      else:
          exists_or_mkdir(path)
          download_file_from_google_drive(drive_id, save_path)
          zip_dir = ''
          with zipfile.ZipFile(save_path) as zf:
              zip_dir = zf.namelist()[0]
              zf.extractall(path)
          os.remove(save_path)
          os.rename(os.path.join(path, zip_dir), image_path)
  
      data_files = load_file_list(path=image_path, regx='\\.jpg', printable=False)
      for i, _v in enumerate(data_files):
          data_files[i] = os.path.join(image_path, data_files[i])
      return data_files
  
  
  def load_voc_dataset(path='data', dataset='2012', contain_classes_in_person=False):
      """Pascal VOC 2007/2012 Dataset.
  
      It has 20 objects:
      aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor
      and additional 3 classes : head, hand, foot for person.
  
      Parameters
      -----------
      path : str
          The path that the data is downloaded to, defaults is ``data/VOC``.
      dataset : str
          The VOC dataset version, `2012`, `2007`, `2007test` or `2012test`. We usually train model on `2007+2012` and test it on `2007test`.
      contain_classes_in_person : boolean
          Whether include head, hand and foot annotation, default is False.
  
      Returns
      ---------
      imgs_file_list : list of str
          Full paths of all images.
      imgs_semseg_file_list : list of str
          Full paths of all maps for semantic segmentation. Note that not all images have this map!
      imgs_insseg_file_list : list of str
          Full paths of all maps for instance segmentation. Note that not all images have this map!
      imgs_ann_file_list : list of str
          Full paths of all annotations for bounding box and object class, all images have this annotations.
      classes : list of str
          Classes in order.
      classes_in_person : list of str
          Classes in person.
      classes_dict : dictionary
          Class label to integer.
      n_objs_list : list of int
          Number of objects in all images in ``imgs_file_list`` in order.
      objs_info_list : list of str
          Darknet format for the annotation of all images in ``imgs_file_list`` in order. ``[class_id x_centre y_centre width height]`` in ratio format.
      objs_info_dicts : dictionary
          The annotation of all images in ``imgs_file_list``, ``{imgs_file_list : dictionary for annotation}``,
          format from `TensorFlow/Models/object-detection <https://github.com/tensorflow/models/blob/master/object_detection/create_pascal_tf_record.py>`__.
  
      Examples
      ----------
      >>> imgs_file_list, imgs_semseg_file_list, imgs_insseg_file_list, imgs_ann_file_list,
      >>>     classes, classes_in_person, classes_dict,
      >>>     n_objs_list, objs_info_list, objs_info_dicts = tl.files.load_voc_dataset(dataset="2012", contain_classes_in_person=False)
      >>> idx = 26
      >>> print(classes)
      ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
      >>> print(classes_dict)
      {'sheep': 16, 'horse': 12, 'bicycle': 1, 'bottle': 4, 'cow': 9, 'sofa': 17, 'car': 6, 'dog': 11, 'cat': 7, 'person': 14, 'train': 18, 'diningtable': 10, 'aeroplane': 0, 'bus': 5, 'pottedplant': 15, 'tvmonitor': 19, 'chair': 8, 'bird': 2, 'boat': 3, 'motorbike': 13}
      >>> print(imgs_file_list[idx])
      data/VOC/VOC2012/JPEGImages/2007_000423.jpg
      >>> print(n_objs_list[idx])
      2
      >>> print(imgs_ann_file_list[idx])
      data/VOC/VOC2012/Annotations/2007_000423.xml
      >>> print(objs_info_list[idx])
      14 0.173 0.461333333333 0.142 0.496
      14 0.828 0.542666666667 0.188 0.594666666667
      >>> ann = tl.prepro.parse_darknet_ann_str_to_list(objs_info_list[idx])
      >>> print(ann)
      [[14, 0.173, 0.461333333333, 0.142, 0.496], [14, 0.828, 0.542666666667, 0.188, 0.594666666667]]
      >>> c, b = tl.prepro.parse_darknet_ann_list_to_cls_box(ann)
      >>> print(c, b)
      [14, 14] [[0.173, 0.461333333333, 0.142, 0.496], [0.828, 0.542666666667, 0.188, 0.594666666667]]
  
      References
      -------------
      - `Pascal VOC2012 Website <http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit>`__.
      - `Pascal VOC2007 Website <http://host.robots.ox.ac.uk/pascal/VOC/voc2007/>`__.
  
      """
      path = os.path.join(path, 'VOC')
  
      def _recursive_parse_xml_to_dict(xml):
          """Recursively parses XML contents to python dict.
  
          We assume that `object` tags are the only ones that can appear
          multiple times at the same level of a tree.
  
          Args:
              xml: xml tree obtained by parsing XML file contents using lxml.etree
  
          Returns:
              Python dictionary holding XML contents.
  
          """
          if not xml:
              # if xml is not None:
              return {xml.tag: xml.text}
          result = {}
          for child in xml:
              child_result = _recursive_parse_xml_to_dict(child)
              if child.tag != 'object':
                  result[child.tag] = child_result[child.tag]
              else:
                  if child.tag not in result:
                      result[child.tag] = []
                  result[child.tag].append(child_result[child.tag])
          return {xml.tag: result}
  
      if dataset == "2012":
          url = "http://host.robots.ox.ac.uk/pascal/VOC/voc2012/"
          tar_filename = "VOCtrainval_11-May-2012.tar"
          extracted_filename = "VOC2012"  #"VOCdevkit/VOC2012"
          logging.info("    [============= VOC 2012 =============]")
      elif dataset == "2012test":
          extracted_filename = "VOC2012test"  #"VOCdevkit/VOC2012"
          logging.info("    [============= VOC 2012 Test Set =============]")
          logging.info(
              "    \nAuthor: 2012test only have person annotation, so 2007test is highly recommended for testing !\n"
          )
          time.sleep(3)
          if os.path.isdir(os.path.join(path, extracted_filename)) is False:
              logging.info("For VOC 2012 Test data - online registration required")
              logging.info(
                  " Please download VOC2012test.tar from:  \n register: http://host.robots.ox.ac.uk:8080 \n voc2012 : http://host.robots.ox.ac.uk:8080/eval/challenges/voc2012/ \ndownload: http://host.robots.ox.ac.uk:8080/eval/downloads/VOC2012test.tar"
              )
              logging.info(" unzip VOC2012test.tar,rename the folder to VOC2012test and put it into %s" % path)
              exit()
          # # http://host.robots.ox.ac.uk:8080/eval/downloads/VOC2012test.tar
          # url = "http://host.robots.ox.ac.uk:8080/eval/downloads/"
          # tar_filename = "VOC2012test.tar"
      elif dataset == "2007":
          url = "http://host.robots.ox.ac.uk/pascal/VOC/voc2007/"
          tar_filename = "VOCtrainval_06-Nov-2007.tar"
          extracted_filename = "VOC2007"
          logging.info("    [============= VOC 2007 =============]")
      elif dataset == "2007test":
          # http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html#testdata
          # http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
          url = "http://host.robots.ox.ac.uk/pascal/VOC/voc2007/"
          tar_filename = "VOCtest_06-Nov-2007.tar"
          extracted_filename = "VOC2007test"
          logging.info("    [============= VOC 2007 Test Set =============]")
      else:
          raise Exception("Please set the dataset aug to 2012, 2012test or 2007.")
  
      # download dataset
      if dataset != "2012test":
          _platform = sys.platform
          if folder_exists(os.path.join(path, extracted_filename)) is False:
              logging.info("[VOC] {} is nonexistent in {}".format(extracted_filename, path))
              maybe_download_and_extract(tar_filename, path, url, extract=True)
              del_file(os.path.join(path, tar_filename))
              if dataset == "2012":
                  if _platform == "win32":
                      os.system("mv {}\VOCdevkit\VOC2012 {}\VOC2012".format(path, path))
                  else:
                      os.system("mv {}/VOCdevkit/VOC2012 {}/VOC2012".format(path, path))
              elif dataset == "2007":
                  if _platform == "win32":
                      os.system("mv {}\VOCdevkit\VOC2007 {}\VOC2007".format(path, path))
                  else:
                      os.system("mv {}/VOCdevkit/VOC2007 {}/VOC2007".format(path, path))
              elif dataset == "2007test":
                  if _platform == "win32":
                      os.system("mv {}\VOCdevkit\VOC2007 {}\VOC2007test".format(path, path))
                  else:
                      os.system("mv {}/VOCdevkit/VOC2007 {}/VOC2007test".format(path, path))
              del_folder(os.path.join(path, 'VOCdevkit'))
      # object classes(labels)  NOTE: YOU CAN CUSTOMIZE THIS LIST
      classes = [
          "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
          "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"
      ]
      if contain_classes_in_person:
          classes_in_person = ["head", "hand", "foot"]
      else:
          classes_in_person = []
  
      classes += classes_in_person  # use extra 3 classes for person
  
      classes_dict = utils.list_string_to_dict(classes)
      logging.info("[VOC] object classes {}".format(classes_dict))
  
      # 1. image path list
      # folder_imgs = path+"/"+extracted_filename+"/JPEGImages/"
      folder_imgs = os.path.join(path, extracted_filename, "JPEGImages")
      imgs_file_list = load_file_list(path=folder_imgs, regx='\\.jpg', printable=False)
      logging.info("[VOC] {} images found".format(len(imgs_file_list)))
  
      imgs_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
                         )  # 2007_000027.jpg --> 2007000027
  
      imgs_file_list = [os.path.join(folder_imgs, s) for s in imgs_file_list]
      # logging.info('IM',imgs_file_list[0::3333], imgs_file_list[-1])
      if dataset != "2012test":
          ##======== 2. semantic segmentation maps path list
          # folder_semseg = path+"/"+extracted_filename+"/SegmentationClass/"
          folder_semseg = os.path.join(path, extracted_filename, "SegmentationClass")
          imgs_semseg_file_list = load_file_list(path=folder_semseg, regx='\\.png', printable=False)
          logging.info("[VOC] {} maps for semantic segmentation found".format(len(imgs_semseg_file_list)))
          imgs_semseg_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
                                    )  # 2007_000032.png --> 2007000032
          imgs_semseg_file_list = [os.path.join(folder_semseg, s) for s in imgs_semseg_file_list]
          # logging.info('Semantic Seg IM',imgs_semseg_file_list[0::333], imgs_semseg_file_list[-1])
          ##======== 3. instance segmentation maps path list
          # folder_insseg = path+"/"+extracted_filename+"/SegmentationObject/"
          folder_insseg = os.path.join(path, extracted_filename, "SegmentationObject")
          imgs_insseg_file_list = load_file_list(path=folder_insseg, regx='\\.png', printable=False)
          logging.info("[VOC] {} maps for instance segmentation found".format(len(imgs_semseg_file_list)))
          imgs_insseg_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
                                    )  # 2007_000032.png --> 2007000032
          imgs_insseg_file_list = [os.path.join(folder_insseg, s) for s in imgs_insseg_file_list]
          # logging.info('Instance Seg IM',imgs_insseg_file_list[0::333], imgs_insseg_file_list[-1])
      else:
          imgs_semseg_file_list = []
          imgs_insseg_file_list = []
      # 4. annotations for bounding box and object class
      # folder_ann = path+"/"+extracted_filename+"/Annotations/"
      folder_ann = os.path.join(path, extracted_filename, "Annotations")
      imgs_ann_file_list = load_file_list(path=folder_ann, regx='\\.xml', printable=False)
      logging.info(
          "[VOC] {} XML annotation files for bounding box and object class found".format(len(imgs_ann_file_list))
      )
      imgs_ann_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
                             )  # 2007_000027.xml --> 2007000027
      imgs_ann_file_list = [os.path.join(folder_ann, s) for s in imgs_ann_file_list]
      # logging.info('ANN',imgs_ann_file_list[0::3333], imgs_ann_file_list[-1])
  
      if dataset == "2012test":  # remove unused images in JPEG folder
          imgs_file_list_new = []
          for ann in imgs_ann_file_list:
              ann = os.path.split(ann)[-1].split('.')[0]
              for im in imgs_file_list:
                  if ann in im:
                      imgs_file_list_new.append(im)
                      break
          imgs_file_list = imgs_file_list_new
          logging.info("[VOC] keep %d images" % len(imgs_file_list_new))
  
      # parse XML annotations
      def convert(size, box):
          dw = 1. / size[0]
          dh = 1. / size[1]
          x = (box[0] + box[1]) / 2.0
          y = (box[2] + box[3]) / 2.0
          w = box[1] - box[0]
          h = box[3] - box[2]
          x = x * dw
          w = w * dw
          y = y * dh
          h = h * dh
          return x, y, w, h
  
      def convert_annotation(file_name):
          """Given VOC2012 XML Annotations, returns number of objects and info."""
          in_file = open(file_name)
          out_file = ""
          tree = ET.parse(in_file)
          root = tree.getroot()
          size = root.find('size')
          w = int(size.find('width').text)
          h = int(size.find('height').text)
          n_objs = 0
  
          for obj in root.iter('object'):
              if dataset != "2012test":
                  difficult = obj.find('difficult').text
                  cls = obj.find('name').text
                  if cls not in classes or int(difficult) == 1:
                      continue
              else:
                  cls = obj.find('name').text
                  if cls not in classes:
                      continue
              cls_id = classes.index(cls)
              xmlbox = obj.find('bndbox')
              b = (
                  float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
                  float(xmlbox.find('ymax').text)
              )
              bb = convert((w, h), b)
  
              out_file += str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n'
              n_objs += 1
              if cls in "person":
                  for part in obj.iter('part'):
                      cls = part.find('name').text
                      if cls not in classes_in_person:
                          continue
                      cls_id = classes.index(cls)
                      xmlbox = part.find('bndbox')
                      b = (
                          float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text),
                          float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)
                      )
                      bb = convert((w, h), b)
                      # out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
                      out_file += str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n'
                      n_objs += 1
          in_file.close()
          return n_objs, out_file
  
      logging.info("[VOC] Parsing xml annotations files")
      n_objs_list = []
      objs_info_list = []  # Darknet Format list of string
      objs_info_dicts = {}
      for idx, ann_file in enumerate(imgs_ann_file_list):
          n_objs, objs_info = convert_annotation(ann_file)
          n_objs_list.append(n_objs)
          objs_info_list.append(objs_info)
          with tf.gfile.GFile(ann_file, 'r') as fid:
              xml_str = fid.read()
          xml = etree.fromstring(xml_str)
          data = _recursive_parse_xml_to_dict(xml)['annotation']
          objs_info_dicts.update({imgs_file_list[idx]: data})
  
      return imgs_file_list, imgs_semseg_file_list, imgs_insseg_file_list, imgs_ann_file_list, classes, classes_in_person, classes_dict, n_objs_list, objs_info_list, objs_info_dicts
  
  
  def load_mpii_pose_dataset(path='data', is_16_pos_only=False):
      """Load MPII Human Pose Dataset.
  
      Parameters
      -----------
      path : str
          The path that the data is downloaded to.
      is_16_pos_only : boolean
          If True, only return the peoples contain 16 pose keypoints. (Usually be used for single person pose estimation)
  
      Returns
      ----------
      img_train_list : list of str
          The image directories of training data.
      ann_train_list : list of dict
          The annotations of training data.
      img_test_list : list of str
          The image directories of testing data.
      ann_test_list : list of dict
          The annotations of testing data.
  
      Examples
      --------
      >>> import pprint
      >>> import tensorlayer as tl
      >>> img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset()
      >>> image = tl.vis.read_image(img_train_list[0])
      >>> tl.vis.draw_mpii_pose_to_image(image, ann_train_list[0], 'image.png')
      >>> pprint.pprint(ann_train_list[0])
  
      References
      -----------
      - `MPII Human Pose Dataset. CVPR 14 <http://human-pose.mpi-inf.mpg.de>`__
      - `MPII Human Pose Models. CVPR 16 <http://pose.mpi-inf.mpg.de>`__
      - `MPII Human Shape, Poselet Conditioned Pictorial Structures and etc <http://pose.mpi-inf.mpg.de/#related>`__
      - `MPII Keyponts and ID <http://human-pose.mpi-inf.mpg.de/#download>`__
      """
      path = os.path.join(path, 'mpii_human_pose')
      logging.info("Load or Download MPII Human Pose > {}".format(path))
  
      # annotation
      url = "http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/"
      tar_filename = "mpii_human_pose_v1_u12_2.zip"
      extracted_filename = "mpii_human_pose_v1_u12_2"
      if folder_exists(os.path.join(path, extracted_filename)) is False:
          logging.info("[MPII] (annotation) {} is nonexistent in {}".format(extracted_filename, path))
          maybe_download_and_extract(tar_filename, path, url, extract=True)
          del_file(os.path.join(path, tar_filename))
  
      # images
      url = "http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/"
      tar_filename = "mpii_human_pose_v1.tar.gz"
      extracted_filename2 = "images"
      if folder_exists(os.path.join(path, extracted_filename2)) is False:
          logging.info("[MPII] (images) {} is nonexistent in {}".format(extracted_filename, path))
          maybe_download_and_extract(tar_filename, path, url, extract=True)
          del_file(os.path.join(path, tar_filename))
  
      # parse annotation, format see http://human-pose.mpi-inf.mpg.de/#download
      logging.info("reading annotations from mat file ...")
      # mat = sio.loadmat(os.path.join(path, extracted_filename, "mpii_human_pose_v1_u12_1.mat"))
  
      # def fix_wrong_joints(joint):    # https://github.com/mitmul/deeppose/blob/master/datasets/mpii_dataset.py
      #     if '12' in joint and '13' in joint and '2' in joint and '3' in joint:
      #         if ((joint['12'][0] < joint['13'][0]) and
      #                 (joint['3'][0] < joint['2'][0])):
      #             joint['2'], joint['3'] = joint['3'], joint['2']
      #         if ((joint['12'][0] > joint['13'][0]) and
      #                 (joint['3'][0] > joint['2'][0])):
      #             joint['2'], joint['3'] = joint['3'], joint['2']
      #     return joint
  
      ann_train_list = []
      ann_test_list = []
      img_train_list = []
      img_test_list = []
  
      def save_joints():
          # joint_data_fn = os.path.join(path, 'data.json')
          # fp = open(joint_data_fn, 'w')
          mat = sio.loadmat(os.path.join(path, extracted_filename, "mpii_human_pose_v1_u12_1.mat"))
  
          for _, (anno, train_flag) in enumerate(  # all images
                  zip(mat['RELEASE']['annolist'][0, 0][0], mat['RELEASE']['img_train'][0, 0][0])):
  
              img_fn = anno['image']['name'][0, 0][0]
              train_flag = int(train_flag)
  
              # print(i, img_fn, train_flag) # DEBUG print all images
  
              if train_flag:
                  img_train_list.append(img_fn)
                  ann_train_list.append([])
              else:
                  img_test_list.append(img_fn)
                  ann_test_list.append([])
  
              head_rect = []
              if 'x1' in str(anno['annorect'].dtype):
                  head_rect = zip(
                      [x1[0, 0] for x1 in anno['annorect']['x1'][0]], [y1[0, 0] for y1 in anno['annorect']['y1'][0]],
                      [x2[0, 0] for x2 in anno['annorect']['x2'][0]], [y2[0, 0] for y2 in anno['annorect']['y2'][0]]
                  )
              else:
                  head_rect = []  # TODO
  
              if 'annopoints' in str(anno['annorect'].dtype):
                  annopoints = anno['annorect']['annopoints'][0]
                  head_x1s = anno['annorect']['x1'][0]
                  head_y1s = anno['annorect']['y1'][0]
                  head_x2s = anno['annorect']['x2'][0]
                  head_y2s = anno['annorect']['y2'][0]
  
                  for annopoint, head_x1, head_y1, head_x2, head_y2 in zip(annopoints, head_x1s, head_y1s, head_x2s,
                                                                           head_y2s):
                      # if annopoint != []:
                      # if len(annopoint) != 0:
                      if annopoint.size:
                          head_rect = [
                              float(head_x1[0, 0]),
                              float(head_y1[0, 0]),
                              float(head_x2[0, 0]),
                              float(head_y2[0, 0])
                          ]
  
                          # joint coordinates
                          annopoint = annopoint['point'][0, 0]
                          j_id = [str(j_i[0, 0]) for j_i in annopoint['id'][0]]
                          x = [x[0, 0] for x in annopoint['x'][0]]
                          y = [y[0, 0] for y in annopoint['y'][0]]
                          joint_pos = {}
                          for _j_id, (_x, _y) in zip(j_id, zip(x, y)):
                              joint_pos[int(_j_id)] = [float(_x), float(_y)]
                          # joint_pos = fix_wrong_joints(joint_pos)
  
                          # visibility list
                          if 'is_visible' in str(annopoint.dtype):
                              vis = [v[0] if v.size > 0 else [0] for v in annopoint['is_visible'][0]]
                              vis = dict([(k, int(v[0])) if len(v) > 0 else v for k, v in zip(j_id, vis)])
                          else:
                              vis = None
  
                          # if len(joint_pos) == 16:
                          if ((is_16_pos_only ==True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
                              # only use image with 16 key points / or use all
                              data = {
                                  'filename': img_fn,
                                  'train': train_flag,
                                  'head_rect': head_rect,
                                  'is_visible': vis,
                                  'joint_pos': joint_pos
                              }
                              # print(json.dumps(data), file=fp)  # py3
                              if train_flag:
                                  ann_train_list[-1].append(data)
                              else:
                                  ann_test_list[-1].append(data)
  
      # def write_line(datum, fp):
      #     joints = sorted([[int(k), v] for k, v in datum['joint_pos'].items()])
      #     joints = np.array([j for i, j in joints]).flatten()
      #
      #     out = [datum['filename']]
      #     out.extend(joints)
      #     out = [str(o) for o in out]
      #     out = ','.join(out)
      #
      #     print(out, file=fp)
  
      # def split_train_test():
      #     # fp_test = open('data/mpii/test_joints.csv', 'w')
      #     fp_test = open(os.path.join(path, 'test_joints.csv'), 'w')
      #     # fp_train = open('data/mpii/train_joints.csv', 'w')
      #     fp_train = open(os.path.join(path, 'train_joints.csv'), 'w')
      #     # all_data = open('data/mpii/data.json').readlines()
      #     all_data = open(os.path.join(path, 'data.json')).readlines()
      #     N = len(all_data)
      #     N_test = int(N * 0.1)
      #     N_train = N - N_test
      #
      #     print('N:{}'.format(N))
      #     print('N_train:{}'.format(N_train))
      #     print('N_test:{}'.format(N_test))
      #
      #     np.random.seed(1701)
      #     perm = np.random.permutation(N)
      #     test_indices = perm[:N_test]
      #     train_indices = perm[N_test:]
      #
      #     print('train_indices:{}'.format(len(train_indices)))
      #     print('test_indices:{}'.format(len(test_indices)))
      #
      #     for i in train_indices:
      #         datum = json.loads(all_data[i].strip())
      #         write_line(datum, fp_train)
      #
      #     for i in test_indices:
      #         datum = json.loads(all_data[i].strip())
      #         write_line(datum, fp_test)
  
      save_joints()
      # split_train_test()  #
  
      ## read images dir
      logging.info("reading images list ...")
      img_dir = os.path.join(path, extracted_filename2)
      _img_list = load_file_list(path=os.path.join(path, extracted_filename2), regx='\\.jpg', printable=False)
      # ann_list = json.load(open(os.path.join(path, 'data.json')))
      for i, im in enumerate(img_train_list):
          if im not in _img_list:
              print('missing training image {} in {} (remove from img(ann)_train_list)'.format(im, img_dir))
              # img_train_list.remove(im)
              del img_train_list[i]
              del ann_train_list[i]
      for i, im in enumerate(img_test_list):
          if im not in _img_list:
              print('missing testing image {} in {} (remove from img(ann)_test_list)'.format(im, img_dir))
              # img_test_list.remove(im)
              del img_train_list[i]
              del ann_train_list[i]
  
      ## check annotation and images
      n_train_images = len(img_train_list)
      n_test_images = len(img_test_list)
      n_images = n_train_images + n_test_images
      logging.info("n_images: {} n_train_images: {} n_test_images: {}".format(n_images, n_train_images, n_test_images))
      n_train_ann = len(ann_train_list)
      n_test_ann = len(ann_test_list)
      n_ann = n_train_ann + n_test_ann
      logging.info("n_ann: {} n_train_ann: {} n_test_ann: {}".format(n_ann, n_train_ann, n_test_ann))
      n_train_people = len(sum(ann_train_list, []))
      n_test_people = len(sum(ann_test_list, []))
      n_people = n_train_people + n_test_people
      logging.info("n_people: {} n_train_people: {} n_test_people: {}".format(n_people, n_train_people, n_test_people))
      # add path to all image file name
      for i, value in enumerate(img_train_list):
          img_train_list[i] = os.path.join(img_dir, value)
      for i, value in enumerate(img_test_list):
          img_test_list[i] = os.path.join(img_dir, value)
      return img_train_list, ann_train_list, img_test_list, ann_test_list
  
  
  def save_npz(save_list=None, name='model.npz', sess=None):
      """Input parameters and the file name, save parameters into .npz file. Use tl.utils.load_npz() to restore.
  
      Parameters
      ----------
      save_list : list of tensor
          A list of parameters (tensor) to be saved.
      name : str
          The name of the `.npz` file.
      sess : None or Session
          Session may be required in some case.
  
      Examples
      --------
      Save model to npz
  
      >>> tl.files.save_npz(network.all_params, name='model.npz', sess=sess)
  
      Load model from npz (Method 1)
  
      >>> load_params = tl.files.load_npz(name='model.npz')
      >>> tl.files.assign_params(sess, load_params, network)
  
      Load model from npz (Method 2)
  
      >>> tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=network)
  
      Notes
      -----
      If you got session issues, you can change the value.eval() to value.eval(session=sess)
  
      References
      ----------
      `Saving dictionary using numpy <http://stackoverflow.com/questions/22315595/saving-dictionary-of-header-information-using-numpy-savez>`__
  
      """
      if save_list is None:
          save_list = []
  
      save_list_var = []
      if sess:
          save_list_var = sess.run(save_list)
      else:
          try:
              save_list_var.extend([v.eval() for v in save_list])
          except Exception:
              logging.info(
                  " Fail to save model, Hint: pass the session into this function, tl.files.save_npz(network.all_params, name='model.npz', sess=sess)"
              )
      np.savez(name, params=save_list_var)
      save_list_var = None
      del save_list_var
      logging.info("[*] %s saved" % name)
  
  
  def load_npz(path='', name='model.npz'):
      """Load the parameters of a Model saved by tl.files.save_npz().
  
      Parameters
      ----------
      path : str
          Folder path to `.npz` file.
      name : str
          The name of the `.npz` file.
  
      Returns
      --------
      list of array
          A list of parameters in order.
  
      Examples
      --------
      - See ``tl.files.save_npz``
  
      References
      ----------
      - `Saving dictionary using numpy <http://stackoverflow.com/questions/22315595/saving-dictionary-of-header-information-using-numpy-savez>`__
  
      """
      d = np.load(path + name)
      return d['params']
  
  
  def assign_params(sess, params, network):
      """Assign the given parameters to the TensorLayer network.
  
      Parameters
      ----------
      sess : Session
          TensorFlow Session.
      params : list of array
          A list of parameters (array) in order.
      network : :class:`Layer`
          The network to be assigned.
  
      Returns
      --------
      list of operations
          A list of tf ops in order that assign params. Support sess.run(ops) manually.
  
      Examples
      --------
      - See ``tl.files.save_npz``
  
      References
      ----------
      - `Assign value to a TensorFlow variable <http://stackoverflow.com/questions/34220532/how-to-assign-value-to-a-tensorflow-variable>`__
  
      """
      ops = []
      for idx, param in enumerate(params):
          ops.append(network.all_params[idx].assign(param))
      if sess is not None:
          sess.run(ops)
      return ops
  
  
  def load_and_assign_npz(sess=None, name=None, network=None):
      """Load model from npz and assign to a network.
  
      Parameters
      -------------
      sess : Session
          TensorFlow Session.
      name : str
          The name of the `.npz` file.
      network : :class:`Layer`
          The network to be assigned.
  
      Returns
      --------
      False or network
          Returns False, if the model is not exist.
  
      Examples
      --------
      - See ``tl.files.save_npz``
  
      """
      if network is None:
          raise ValueError("network is None.")
      if sess is None:
          raise ValueError("session is None.")
      if not os.path.exists(name):
          logging.info("[!] Load {} failed!".format(name))
          return False
      else:
          params = load_npz(name=name)
          assign_params(sess, params, network)
          logging.info("[*] Load {} SUCCESS!".format(name))
          return network
  
  
  def save_npz_dict(save_list=None, name='model.npz', sess=None):
      """Input parameters and the file name, save parameters as a dictionary into .npz file.
  
      Use ``tl.files.load_and_assign_npz_dict()`` to restore.
  
      Parameters
      ----------
      save_list : list of parameters
          A list of parameters (tensor) to be saved.
      name : str
          The name of the `.npz` file.
      sess : Session
          TensorFlow Session.
  
      """
      if sess is None:
          raise ValueError("session is None.")
      if save_list is None:
          save_list = []
  
      save_list_names = [tensor.name for tensor in save_list]
      save_list_var = sess.run(save_list)
      save_var_dict = {save_list_names[idx]: val for idx, val in enumerate(save_list_var)}
      np.savez(name, **save_var_dict)
      save_list_var = None
      save_var_dict = None
      del save_list_var
      del save_var_dict
      logging.info("[*] Model saved in npz_dict %s" % name)
  
  
  def load_and_assign_npz_dict(name='model.npz', sess=None):
      """Restore the parameters saved by ``tl.files.save_npz_dict()``.
  
      Parameters
      ----------
      name : str
          The name of the `.npz` file.
      sess : Session
          TensorFlow Session.
  
      """
      if sess is None:
          raise ValueError("session is None.")
  
      if not os.path.exists(name):
          logging.info("[!] Load {} failed!".format(name))
          return False
  
      params = np.load(name)
      if len(params.keys()) != len(set(params.keys())):
          raise Exception("Duplication in model npz_dict %s" % name)
      ops = list()
      for key in params.keys():
          try:
              # tensor = tf.get_default_graph().get_tensor_by_name(key)
              # varlist = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=key)
              varlist = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=key)
              if len(varlist) > 1:
                  raise Exception("[!] Multiple candidate variables to be assigned for name %s" % key)
              elif len(varlist) == 0:
                  raise KeyError
              else:
                  ops.append(varlist[0].assign(params[key]))
                  logging.info("[*] params restored: %s" % key)
          except KeyError:
              logging.info("[!] Warning: Tensor named %s not found in network." % key)
  
      sess.run(ops)
      logging.info("[*] Model restored from npz_dict %s" % name)
  
  
  def save_ckpt(
          sess=None, mode_name='model.ckpt', save_dir='checkpoint', var_list=None, global_step=None, printable=False
  ):
      """Save parameters into `ckpt` file.
  
      Parameters
      ------------
      sess : Session
          TensorFlow Session.
      mode_name : str
          The name of the model, default is ``model.ckpt``.
      save_dir : str
          The path / file directory to the `ckpt`, default is ``checkpoint``.
      var_list : list of tensor
          The parameters / variables (tensor) to be saved. If empty, save all global variables (default).
      global_step : int or None
          Step number.
      printable : boolean
          Whether to print all parameters information.
  
      See Also
      --------
      load_ckpt
  
      """
      if sess is None:
          raise ValueError("session is None.")
      if var_list is None:
          var_list = []
  
      ckpt_file = os.path.join(save_dir, mode_name)
      if var_list == []:
          var_list = tf.global_variables()
  
      logging.info("[*] save %s n_params: %d" % (ckpt_file, len(var_list)))
  
      if printable:
          for idx, v in enumerate(var_list):
              logging.info("  param {:3}: {:15}   {}".format(idx, v.name, str(v.get_shape())))
  
      saver = tf.train.Saver(var_list)
      saver.save(sess, ckpt_file, global_step=global_step)
  
  
  def load_ckpt(sess=None, mode_name='model.ckpt', save_dir='checkpoint', var_list=None, is_latest=True, printable=False):
      """Load parameters from `ckpt` file.
  
      Parameters
      ------------
      sess : Session
          TensorFlow Session.
      mode_name : str
          The name of the model, default is ``model.ckpt``.
      save_dir : str
          The path / file directory to the `ckpt`, default is ``checkpoint``.
      var_list : list of tensor
          The parameters / variables (tensor) to be saved. If empty, save all global variables (default).
      is_latest : boolean
          Whether to load the latest `ckpt`, if False, load the `ckpt` with the name of ```mode_name``.
      printable : boolean
          Whether to print all parameters information.
  
      Examples
      ----------
      Save all global parameters.
  
      >>> tl.files.save_ckpt(sess=sess, mode_name='model.ckpt', save_dir='model', printable=True)
  
      Save specific parameters.
  
      >>> tl.files.save_ckpt(sess=sess, mode_name='model.ckpt', var_list=net.all_params, save_dir='model', printable=True)
  
      Load latest ckpt.
  
      >>> tl.files.load_ckpt(sess=sess, var_list=net.all_params, save_dir='model', printable=True)
  
      Load specific ckpt.
  
      >>> tl.files.load_ckpt(sess=sess, mode_name='model.ckpt', var_list=net.all_params, save_dir='model', is_latest=False, printable=True)
  
      """
      if sess is None:
          raise ValueError("session is None.")
      if var_list is None:
          var_list = []
  
      if is_latest:
          ckpt_file = tf.train.latest_checkpoint(save_dir)
      else:
          ckpt_file = os.path.join(save_dir, mode_name)
  
      if not var_list:
          var_list = tf.global_variables()
  
      logging.info("[*] load %s n_params: %d" % (ckpt_file, len(var_list)))
  
      if printable:
          for idx, v in enumerate(var_list):
              logging.info("  param {:3}: {:15}   {}".format(idx, v.name, str(v.get_shape())))
  
      try:
          saver = tf.train.Saver(var_list)
          saver.restore(sess, ckpt_file)
      except Exception as e:
          logging.info(e)
          logging.info("[*] load ckpt fail ...")
  
  
  def save_any_to_npy(save_dict=None, name='file.npy'):
      """Save variables to `.npy` file.
  
      Parameters
      ------------
      save_dict : directory
          The variables to be saved.
      name : str
          File name.
  
      Examples
      ---------
      >>> tl.files.save_any_to_npy(save_dict={'data': ['a','b']}, name='test.npy')
      >>> data = tl.files.load_npy_to_any(name='test.npy')
      >>> print(data)
      {'data': ['a','b']}
  
      """
      if save_dict is None:
          save_dict = {}
      np.save(name, save_dict)
  
  
  def load_npy_to_any(path='', name='file.npy'):
      """Load `.npy` file.
  
      Parameters
      ------------
      path : str
          Path to the file (optional).
      name : str
          File name.
  
      Examples
      ---------
      - see tl.files.save_any_to_npy()
  
      """
      file_path = os.path.join(path, name)
      try:
          return np.load(file_path).item()
      except Exception:
          return np.load(file_path)
      raise Exception("[!] Fail to load %s" % file_path)
  
  
  def file_exists(filepath):
      """Check whether a file exists by given file path."""
      return os.path.isfile(filepath)
  
  
  def folder_exists(folderpath):
      """Check whether a folder exists by given folder path."""
      return os.path.isdir(folderpath)
  
  
  def del_file(filepath):
      """Delete a file by given file path."""
      os.remove(filepath)
  
  
  def del_folder(folderpath):
      """Delete a folder by given folder path."""
      shutil.rmtree(folderpath)
  
  
  def read_file(filepath):
      """Read a file and return a string.
  
      Examples
      ---------
      >>> data = tl.files.read_file('data.txt')
  
      """
      with open(filepath, 'r') as afile:
          return afile.read()
  
  
  def load_file_list(path=None, regx='\.npz', printable=True):
      r"""Return a file list in a folder by given a path and regular expression.
  
      Parameters
      ----------
      path : str or None
          A folder path, if `None`, use the current directory.
      regx : str
          The regx of file name.
      printable : boolean
          Whether to print the files infomation.
  
      Examples
      ----------
      >>> file_list = tl.files.load_file_list(path=None, regx='w1pre_[0-9]+\.(npz)')
  
      """
      if path is None:
          path = os.getcwd()
      file_list = os.listdir(path)
      return_list = []
      for _, f in enumerate(file_list):
          if re.search(regx, f):
              return_list.append(f)
      # return_list.sort()
      if printable:
          logging.info('Match file list = %s' % return_list)
          logging.info('Number of files = %d' % len(return_list))
      return return_list
  
  
  def load_folder_list(path=""):
      """Return a folder list in a folder by given a folder path.
  
      Parameters
      ----------
      path : str
          A folder path.
  
      """
      return [os.path.join(path, o) for o in os.listdir(path) if os.path.isdir(os.path.join(path, o))]
  
  
  def exists_or_mkdir(path, verbose=True):
      """Check a folder by given name, if not exist, create the folder and return False,
      if directory exists, return True.
  
      Parameters
      ----------
      path : str
          A folder path.
      verbose : boolean
          If True (default), prints results.
  
      Returns
      --------
      boolean
          True if folder already exist, otherwise, returns False and create the folder.
  
      Examples
      --------
      >>> tl.files.exists_or_mkdir("checkpoints/train")
  
      """
      if not os.path.exists(path):
          if verbose:
              logging.info("[*] creates %s ..." % path)
          os.makedirs(path)
          return False
      else:
          if verbose:
              logging.info("[!] %s exists ..." % path)
          return True
  
  
  def maybe_download_and_extract(filename, working_directory, url_source, extract=False, expected_bytes=None):
      """Checks if file exists in working_directory otherwise tries to dowload the file,
      and optionally also tries to extract the file if format is ".zip" or ".tar"
  
      Parameters
      -----------
      filename : str
          The name of the (to be) dowloaded file.
      working_directory : str
          A folder path to search for the file in and dowload the file to
      url : str
          The URL to download the file from
      extract : boolean
          If True, tries to uncompress the dowloaded file is ".tar.gz/.tar.bz2" or ".zip" file, default is False.
      expected_bytes : int or None
          If set tries to verify that the downloaded file is of the specified size, otherwise raises an Exception, defaults is None which corresponds to no check being performed.
  
      Returns
      ----------
      str
          File path of the dowloaded (uncompressed) file.
  
      Examples
      --------
      >>> down_file = tl.files.maybe_download_and_extract(filename='train-images-idx3-ubyte.gz',
      ...                                            working_directory='data/',
      ...                                            url_source='http://yann.lecun.com/exdb/mnist/')
      >>> tl.files.maybe_download_and_extract(filename='ADEChallengeData2016.zip',
      ...                                             working_directory='data/',
      ...                                             url_source='http://sceneparsing.csail.mit.edu/data/',
      ...                                             extract=True)
  
      """
  
      # We first define a download function, supporting both Python 2 and 3.
      def _download(filename, working_directory, url_source):
  
          progress_bar = progressbar.ProgressBar()
  
          def _dlProgress(count, blockSize, totalSize, pbar=progress_bar):
              if (totalSize != 0):
  
                  if not pbar.max_value:
                      totalBlocks = math.ceil(float(totalSize) / float(blockSize))
                      pbar.max_value = int(totalBlocks)
  
                  pbar.update(count, force=True)
  
          filepath = os.path.join(working_directory, filename)
  
          logging.info('Downloading %s...\n' % filename)
  
          urlretrieve(url_source + filename, filepath, reporthook=_dlProgress)
  
      exists_or_mkdir(working_directory, verbose=False)
      filepath = os.path.join(working_directory, filename)
  
      if not os.path.exists(filepath):
          _download(filename, working_directory, url_source)
          statinfo = os.stat(filepath)
          logging.info('Succesfully downloaded %s %s bytes.' % (filename, statinfo.st_size))  #, 'bytes.')
          if (not (expected_bytes is None) and (expected_bytes != statinfo.st_size)):
              raise Exception('Failed to verify ' + filename + '. Can you get to it with a browser?')
          if (extract):
              if tarfile.is_tarfile(filepath):
                  logging.info('Trying to extract tar file')
                  tarfile.open(filepath, 'r').extractall(working_directory)
                  logging.info('... Success!')
              elif zipfile.is_zipfile(filepath):
                  logging.info('Trying to extract zip file')
                  with zipfile.ZipFile(filepath) as zf:
                      zf.extractall(working_directory)
                  logging.info('... Success!')
              else:
                  logging.info("Unknown compression_format only .tar.gz/.tar.bz2/.tar and .zip supported")
      return filepath
  
  
  def natural_keys(text):
      """Sort list of string with number in human order.
  
      Examples
      ----------
      >>> l = ['im1.jpg', 'im31.jpg', 'im11.jpg', 'im21.jpg', 'im03.jpg', 'im05.jpg']
      >>> l.sort(key=tl.files.natural_keys)
      ['im1.jpg', 'im03.jpg', 'im05', 'im11.jpg', 'im21.jpg', 'im31.jpg']
      >>> l.sort() # that is what we dont want
      ['im03.jpg', 'im05', 'im1.jpg', 'im11.jpg', 'im21.jpg', 'im31.jpg']
  
      References
      ----------
      - `link <http://nedbatchelder.com/blog/200712/human_sorting.html>`__
  
      """
  
      # - alist.sort(key=natural_keys) sorts in human order
      # http://nedbatchelder.com/blog/200712/human_sorting.html
      # (See Toothy's implementation in the comments)
      def atoi(text):
          return int(text) if text.isdigit() else text
  
      return [atoi(c) for c in re.split('(\d+)', text)]
  
  
  # Visualizing npz files
  def npz_to_W_pdf(path=None, regx='w1pre_[0-9]+\.(npz)'):
      r"""Convert the first weight matrix of `.npz` file to `.pdf` by using `tl.visualize.W()`.
  
      Parameters
      ----------
      path : str
          A folder path to `npz` files.
      regx : str
          Regx for the file name.
  
      Examples
      ---------
      Convert the first weight matrix of w1_pre...npz file to w1_pre...pdf.
  
      >>> tl.files.npz_to_W_pdf(path='/Users/.../npz_file/', regx='w1pre_[0-9]+\.(npz)')
  
      """
      file_list = load_file_list(path=path, regx=regx)
      for f in file_list:
          W = load_npz(path, f)[0]
          logging.info("%s --> %s" % (f, f.split('.')[0] + '.pdf'))
          visualize.draw_weights(W, second=10, saveable=True, name=f.split('.')[0], fig_idx=2012)
  
apis: numpy.save numpy.random.seed numpy.asarray matplotlib.pyplot.imshow numpy.vstack matplotlib.pyplot.pause numpy.transpose matplotlib.pyplot.figure numpy.savez tensorflow.gfile.GFile matplotlib.pyplot.gca scipy.io.loadmat numpy.load numpy.tile matplotlib.pyplot.draw tensorflow.get_collection numpy.float32 tensorflow.global_variables matplotlib.pyplot.NullLocator tensorflow.train.Saver matplotlib.pyplot.ion numpy.random.shuffle numpy.squeeze tensorflow.python.platform.gfile.Exists tensorflow.train.latest_checkpoint numpy.array numpy.concatenate