#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import os
import numpy as np
import re
import sys
import tarfile
import gzip
import zipfile
from . import visualize
from . import nlp
import pickle
from six.moves import urllib
from six.moves import cPickle
from six.moves import zip
from tensorflow.python.platform import gfile
## Load dataset functions
[docs]def load_mnist_dataset(shape=(-1,784), path="data/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, defaults to (-1,784)
path : string
Path to download data to, defaults to data/mnist/
Examples
--------
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1,784))
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
"""
# We first define functions for loading MNIST 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, 'http://yann.lecun.com/exdb/mnist/')
print(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, 'http://yann.lecun.com/exdb/mnist/')
# 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.
print("Load or Download MNIST > {}".format(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
[docs]def load_cifar10_dataset(shape=(-1, 32, 32, 3), path='data/cifar10/', plotable=False, second=3):
"""The CIFAR-10 dataset 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) , (-1, 32, 32, 3) , (-1, 32*32*3)
plotable : True, False
Whether to plot some image examples.
second : int
If ``plotable`` is True, ``second`` is the display time.
path : string
Path to download data to, defaults to data/cifar10/
Examples
--------
>>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=True)
Notes
------
CIFAR-10 images can only be display without color change under uint8.
>>> X_train = np.asarray(X_train, dtype=np.uint8)
>>> plt.ion()
>>> fig = plt.figure(1232)
>>> count = 1
>>> for row in range(10):
>>> for col in range(10):
>>> a = fig.add_subplot(10, 10, count)
>>> plt.imshow(X_train[count-1], interpolation='nearest')
>>> 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()
>>> plt.pause(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>`_
- `Code references <https://teratail.com/questions/28932>`_
"""
print("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 == True:
print('\nCIFAR-10')
import matplotlib.pyplot as plt
fig = plt.figure(1)
print('Shape of a training image: X_train[0]',X_train[0].shape)
plt.ion() # interactive mode
count = 1
for row in range(10):
for col in range(10):
a = 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
print("X_train:",X_train.shape)
print("y_train:",y_train.shape)
print("X_test:",X_test.shape)
print("y_test:",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
[docs]def load_ptb_dataset(path='data/ptb/'):
"""Penn TreeBank (PTB) dataset 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.
In "Recurrent Neural Network Regularization", they trained regularized LSTMs
of two sizes; these are denoted the medium LSTM and large LSTM. Both LSTMs
have two layers and are unrolled for 35 steps. They initialize the hidden
states to zero. They then use the final hidden states of the current
minibatch as the initial hidden state of the subsequent minibatch
(successive minibatches sequentially traverse the training set).
The size of each minibatch is 20.
The medium LSTM has 650 units per layer and its parameters are initialized
uniformly in [−0.05, 0.05]. They apply 50% dropout on the non-recurrent
connections. They train the LSTM for 39 epochs with a learning rate of 1,
and after 6 epochs they decrease it by a factor of 1.2 after each epoch.
They clip the norm of the gradients (normalized by minibatch size) at 5.
The large LSTM has 1500 units per layer and its parameters are initialized
uniformly in [−0.04, 0.04]. We apply 65% dropout on the non-recurrent
connections. They train the model for 55 epochs with a learning rate of 1;
after 14 epochs they start to reduce the learning rate by a factor of 1.15
after each epoch. They clip the norm of the gradients (normalized by
minibatch size) at 10.
Parameters
----------
path : : string
Path to download data to, defaults to data/ptb/
Returns
--------
train_data, valid_data, test_data, vocabulary size
Examples
--------
>>> train_data, valid_data, test_data, vocab_size = tl.files.load_ptb_dataset()
Code References
---------------
- ``tensorflow.models.rnn.ptb import reader``
Download Links
---------------
- `Manual download <http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz>`_
"""
print("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)
vocabulary = len(word_to_id)
# print(nlp.read_words(train_path)) # ... 'according', 'to', 'mr.', '<unk>', '<eos>']
# print(train_data) # ... 214, 5, 23, 1, 2]
# print(word_to_id) # ... 'beyond': 1295, 'anti-nuclear': 9599, 'trouble': 1520, '<eos>': 2 ... }
# print(vocabulary) # 10000
# exit()
return train_data, valid_data, test_data, vocabulary
[docs]def load_matt_mahoney_text8_dataset(path='data/mm_test8/'):
"""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 : : string
Path to download data to, defaults to data/mm_test8/
Returns
--------
word_list : a list
a list of string (word).\n
e.g. [.... 'their', 'families', 'who', 'were', 'expelled', 'from', 'jerusalem', ...]
Examples
--------
>>> words = tl.files.load_matt_mahoney_text8_dataset()
>>> print('Data size', len(words))
"""
print("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()
return word_list
def load_imdb_dataset(path='data/imdb/', 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 : : string
Path to download data to, defaults to data/imdb/
Examples
--------
>>> X_train, y_train, X_test, y_test = tl.files.load_imbd_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>`_
"""
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
[docs]def load_nietzsche_dataset(path='data/nietzsche/'):
"""Load Nietzsche dataset.
Returns a string.
Parameters
----------
path : string
Path to download data to, defaults to data/nietzsche/
Examples
--------
>>> see tutorial_generate_text.py
>>> words = tl.files.load_nietzsche_dataset()
>>> words = basic_clean_str(words)
>>> words = words.split()
"""
print("Load or Download nietzsche dataset > {}".format(path))
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
[docs]def load_wmt_en_fr_dataset(path='data/wmt_en_fr/'):
"""It will download English-to-French translation 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 : string
Path to download data to, defaults to 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.
"""
# 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."""
print("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")):
print("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
print("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
## Load and save network
[docs]def save_npz(save_list=[], 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 : a list
Parameters want to be saved.
name : a string or None
The name of the .npz file.
sess : None or Session
Examples
--------
>>> tl.files.save_npz(network.all_params, name='model_test.npz', sess=sess)
... File saved to: model_test.npz
>>> load_params = tl.files.load_npz(name='model_test.npz')
... Loading param0, (784, 800)
... Loading param1, (800,)
... Loading param2, (800, 800)
... Loading param3, (800,)
... Loading param4, (800, 10)
... Loading param5, (10,)
>>> put parameters into a TensorLayer network, please see assign_params()
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>`_
"""
## save params into a list
save_list_var = []
if sess:
save_list_var = sess.run(save_list)
else:
try:
for k, value in enumerate(save_list):
save_list_var.append(value.eval())
except:
print(" Fail to save model, Hint: pass the session into this function, 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
print('Model is saved to: %s' % name)
## save params into a dictionary
# rename_dict = {}
# for k, value in enumerate(save_dict):
# rename_dict.update({'param'+str(k) : value.eval()})
# np.savez(name, **rename_dict)
# print('Model is saved to: %s' % name)
[docs]def load_npz(path='', name='model.npz'):
"""Load the parameters of a Model saved by tl.files.save_npz().
Parameters
----------
path : a string
Folder path to .npz file.
name : a string or None
The name of the .npz file.
Returns
--------
params : list
A list of parameters in order.
Examples
--------
- See save_npz and assign_params
References
----------
- `Saving dictionary using numpy <http://stackoverflow.com/questions/22315595/saving-dictionary-of-header-information-using-numpy-savez>`_
"""
## if save_npz save params into a dictionary
# d = np.load( path+name )
# params = []
# print('Load Model')
# for key, val in sorted( d.items() ):
# params.append(val)
# print('Loading %s, %s' % (key, str(val.shape)))
# return params
## if save_npz save params into a list
d = np.load( path+name )
# for val in sorted( d.items() ):
# params = val
# return params
return d['params']
# print(d.items()[0][1]['params'])
# exit()
# return d.items()[0][1]['params']
[docs]def assign_params(sess, params, network):
"""Assign the given parameters to the TensorLayer network.
Parameters
----------
sess : TensorFlow Session
params : a list
A list of parameters in order.
network : a :class:`Layer` class
The network to be assigned
Examples
--------
>>> Save your network as follow:
>>> tl.files.save_npz(network.all_params, name='model_test.npz')
>>> network.print_params()
...
... Next time, load and assign your network as follow:
>>> sess.run(tf.initialize_all_variables()) # re-initialize, then save and assign
>>> load_params = tl.files.load_npz(name='model_test.npz')
>>> tl.files.assign_params(sess, load_params, network)
>>> network.print_params()
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))
sess.run(ops)
[docs]def load_and_assign_npz(sess=None, name=None, network=None):
"""Load model from npz and assign to a network.
Parameters
-------------
sess : TensorFlow Session
name : string
Model path.
network : a :class:`Layer` class
The network to be assigned
Examples
---------
>>> tl.files.load_and_assign_npz(sess=sess, name='net.npz', network=net)
"""
assert network is not None
assert sess is not None
if not os.path.exists(name):
print("[!] Load {} failed!".format(name))
return False
else:
params = tl.files.load_npz(name=name)
tl.files.assign_params(sess, params, network)
print("[*] Load {} SUCCESS!".format(name))
return network
# Load and save variables
[docs]def save_any_to_npy(save_dict={}, name='any.npy'):
"""Save variables to .npy file.
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']}
"""
np.save(name, save_dict)
[docs]def load_npy_to_any(path='', name='any.npy'):
"""Load .npy file.
Examples
---------
- see save_any_to_npy()
"""
try:
npy = np.load(path+name).item()
except:
npy = np.load(path+name)
finally:
return npy
# Visualizing npz files
[docs]def npz_to_W_pdf(path=None, regx='w1pre_[0-9]+\.(npz)'):
"""Convert the first weight matrix of .npz file to .pdf by using tl.visualize.W().
Parameters
----------
path : a string or None
A folder path to npz files.
regx : a string
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]
print("%s --> %s" % (f, f.split('.')[0]+'.pdf'))
visualize.W(W, second=10, saveable=True, name=f.split('.')[0], fig_idx=2012)
## Helper functions
[docs]def load_file_list(path=None, regx='\.npz', printable=True):
"""Return a file list in a folder by given a path and regular expression.
Parameters
----------
path : a string or None
A folder path.
regx : a string
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 == False:
path = os.getcwd()
file_list = os.listdir(path)
return_list = []
for idx, f in enumerate(file_list):
if re.search(regx, f):
return_list.append(f)
# return_list.sort()
if printable:
print('Match file list = %s' % return_list)
print('Number of files = %d' % len(return_list))
return return_list
[docs]def load_folder_list(path=""):
"""Return a folder list in a folder by given a folder path.
Parameters
----------
path : a string or None
A folder path.
"""
return [os.path.join(path,o) for o in os.listdir(path) if os.path.isdir(os.path.join(path,o))]
[docs]def exists_or_mkdir(path, verbose=True):
"""Check a directory, if not exist, create the folder and return False,
if directory exists, return True.
Parameters
----------
path : a string
A folder path.
verbose : boolean
If true prints results, deaults to True
Examples
--------
>>> tl.files.exists_or_mkdir("checkpoints/train")
"""
if not os.path.exists(path):
if verbose:
print("[!] Create %s ..." % path)
os.makedirs(path)
return False
else:
if verbose:
print("[*] %s exists ..." % path)
return True
[docs]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 : string
The name of the (to be) dowloaded file.
working_directory : string
A folder path to search for the file in and dowload the file to
url : string
The URL to download the file from
extract : bool, defaults to False
If True, tries to uncompress the dowloaded file is ".tar.gz/.tar.bz2" or ".zip" file
expected_bytes : int/None
If set tries to verify that the downloaded file is of the specified size, otherwise raises an Exception,
defaults to None which corresponds to no check being performed
Returns
----------
filepath to 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):
def _dlProgress(count, blockSize, totalSize):
if(totalSize != 0):
percent = float(count * blockSize) / float(totalSize) * 100.0
sys.stdout.write("\r" "Downloading " + filename + "...%d%%" % percent)
sys.stdout.flush()
if sys.version_info[0] == 2:
from urllib import urlretrieve
else:
from urllib.request import urlretrieve
filepath = os.path.join(working_directory, 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)
print()
statinfo = os.stat(filepath)
print('Succesfully downloaded', 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):
print('Trying to extract tar file')
tarfile.open(filepath, 'r').extractall(working_directory)
print('... Success!')
elif zipfile.is_zipfile(filepath):
print('Trying to extract zip file')
with zipfile.ZipFile(filepath) as zf:
zf.extractall(working_directory)
print('... Success!')
else:
print("Unknown compression_format only .tar.gz/.tar.bz2/.tar and .zip supported")
return filepath