1# Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15"""Functions for downloading and reading MNIST data (deprecated). 16 17This module and all its submodules are deprecated. 18""" 19 20from __future__ import absolute_import 21from __future__ import division 22from __future__ import print_function 23 24import collections 25import gzip 26import os 27 28 29import numpy 30from six.moves import urllib 31from six.moves import xrange # pylint: disable=redefined-builtin 32 33from tensorflow.python.framework import dtypes 34from tensorflow.python.framework import random_seed 35from tensorflow.python.platform import gfile 36from tensorflow.python.util.deprecation import deprecated 37 38_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test']) 39 40# CVDF mirror of http://yann.lecun.com/exdb/mnist/ 41DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/' 42 43 44def _read32(bytestream): 45 dt = numpy.dtype(numpy.uint32).newbyteorder('>') 46 return numpy.frombuffer(bytestream.read(4), dtype=dt)[0] 47 48 49@deprecated(None, 'Please use tf.data to implement this functionality.') 50def _extract_images(f): 51 """Extract the images into a 4D uint8 numpy array [index, y, x, depth]. 52 53 Args: 54 f: A file object that can be passed into a gzip reader. 55 56 Returns: 57 data: A 4D uint8 numpy array [index, y, x, depth]. 58 59 Raises: 60 ValueError: If the bytestream does not start with 2051. 61 62 """ 63 print('Extracting', f.name) 64 with gzip.GzipFile(fileobj=f) as bytestream: 65 magic = _read32(bytestream) 66 if magic != 2051: 67 raise ValueError('Invalid magic number %d in MNIST image file: %s' % 68 (magic, f.name)) 69 num_images = _read32(bytestream) 70 rows = _read32(bytestream) 71 cols = _read32(bytestream) 72 buf = bytestream.read(rows * cols * num_images) 73 data = numpy.frombuffer(buf, dtype=numpy.uint8) 74 data = data.reshape(num_images, rows, cols, 1) 75 return data 76 77 78@deprecated(None, 'Please use tf.one_hot on tensors.') 79def _dense_to_one_hot(labels_dense, num_classes): 80 """Convert class labels from scalars to one-hot vectors.""" 81 num_labels = labels_dense.shape[0] 82 index_offset = numpy.arange(num_labels) * num_classes 83 labels_one_hot = numpy.zeros((num_labels, num_classes)) 84 labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 85 return labels_one_hot 86 87 88@deprecated(None, 'Please use tf.data to implement this functionality.') 89def _extract_labels(f, one_hot=False, num_classes=10): 90 """Extract the labels into a 1D uint8 numpy array [index]. 91 92 Args: 93 f: A file object that can be passed into a gzip reader. 94 one_hot: Does one hot encoding for the result. 95 num_classes: Number of classes for the one hot encoding. 96 97 Returns: 98 labels: a 1D uint8 numpy array. 99 100 Raises: 101 ValueError: If the bystream doesn't start with 2049. 102 """ 103 print('Extracting', f.name) 104 with gzip.GzipFile(fileobj=f) as bytestream: 105 magic = _read32(bytestream) 106 if magic != 2049: 107 raise ValueError('Invalid magic number %d in MNIST label file: %s' % 108 (magic, f.name)) 109 num_items = _read32(bytestream) 110 buf = bytestream.read(num_items) 111 labels = numpy.frombuffer(buf, dtype=numpy.uint8) 112 if one_hot: 113 return _dense_to_one_hot(labels, num_classes) 114 return labels 115 116 117class _DataSet(object): 118 """Container class for a _DataSet (deprecated). 119 120 THIS CLASS IS DEPRECATED. 121 """ 122 123 @deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py' 124 ' from tensorflow/models.') 125 def __init__(self, 126 images, 127 labels, 128 fake_data=False, 129 one_hot=False, 130 dtype=dtypes.float32, 131 reshape=True, 132 seed=None): 133 """Construct a _DataSet. 134 135 one_hot arg is used only if fake_data is true. `dtype` can be either 136 `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into 137 `[0, 1]`. Seed arg provides for convenient deterministic testing. 138 139 Args: 140 images: The images 141 labels: The labels 142 fake_data: Ignore images and labels, use fake data. 143 one_hot: Bool, return the labels as one hot vectors (if True) or ints (if 144 False). 145 dtype: Output image dtype. One of [uint8, float32]. `uint8` output has 146 range [0,255]. float32 output has range [0,1]. 147 reshape: Bool. If True returned images are returned flattened to vectors. 148 seed: The random seed to use. 149 """ 150 seed1, seed2 = random_seed.get_seed(seed) 151 # If op level seed is not set, use whatever graph level seed is returned 152 numpy.random.seed(seed1 if seed is None else seed2) 153 dtype = dtypes.as_dtype(dtype).base_dtype 154 if dtype not in (dtypes.uint8, dtypes.float32): 155 raise TypeError('Invalid image dtype %r, expected uint8 or float32' % 156 dtype) 157 if fake_data: 158 self._num_examples = 10000 159 self.one_hot = one_hot 160 else: 161 assert images.shape[0] == labels.shape[0], ( 162 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) 163 self._num_examples = images.shape[0] 164 165 # Convert shape from [num examples, rows, columns, depth] 166 # to [num examples, rows*columns] (assuming depth == 1) 167 if reshape: 168 assert images.shape[3] == 1 169 images = images.reshape(images.shape[0], 170 images.shape[1] * images.shape[2]) 171 if dtype == dtypes.float32: 172 # Convert from [0, 255] -> [0.0, 1.0]. 173 images = images.astype(numpy.float32) 174 images = numpy.multiply(images, 1.0 / 255.0) 175 self._images = images 176 self._labels = labels 177 self._epochs_completed = 0 178 self._index_in_epoch = 0 179 180 @property 181 def images(self): 182 return self._images 183 184 @property 185 def labels(self): 186 return self._labels 187 188 @property 189 def num_examples(self): 190 return self._num_examples 191 192 @property 193 def epochs_completed(self): 194 return self._epochs_completed 195 196 def next_batch(self, batch_size, fake_data=False, shuffle=True): 197 """Return the next `batch_size` examples from this data set.""" 198 if fake_data: 199 fake_image = [1] * 784 200 if self.one_hot: 201 fake_label = [1] + [0] * 9 202 else: 203 fake_label = 0 204 return [fake_image for _ in xrange(batch_size) 205 ], [fake_label for _ in xrange(batch_size)] 206 start = self._index_in_epoch 207 # Shuffle for the first epoch 208 if self._epochs_completed == 0 and start == 0 and shuffle: 209 perm0 = numpy.arange(self._num_examples) 210 numpy.random.shuffle(perm0) 211 self._images = self.images[perm0] 212 self._labels = self.labels[perm0] 213 # Go to the next epoch 214 if start + batch_size > self._num_examples: 215 # Finished epoch 216 self._epochs_completed += 1 217 # Get the rest examples in this epoch 218 rest_num_examples = self._num_examples - start 219 images_rest_part = self._images[start:self._num_examples] 220 labels_rest_part = self._labels[start:self._num_examples] 221 # Shuffle the data 222 if shuffle: 223 perm = numpy.arange(self._num_examples) 224 numpy.random.shuffle(perm) 225 self._images = self.images[perm] 226 self._labels = self.labels[perm] 227 # Start next epoch 228 start = 0 229 self._index_in_epoch = batch_size - rest_num_examples 230 end = self._index_in_epoch 231 images_new_part = self._images[start:end] 232 labels_new_part = self._labels[start:end] 233 return numpy.concatenate((images_rest_part, images_new_part), 234 axis=0), numpy.concatenate( 235 (labels_rest_part, labels_new_part), axis=0) 236 else: 237 self._index_in_epoch += batch_size 238 end = self._index_in_epoch 239 return self._images[start:end], self._labels[start:end] 240 241 242@deprecated(None, 'Please write your own downloading logic.') 243def _maybe_download(filename, work_directory, source_url): 244 """Download the data from source url, unless it's already here. 245 246 Args: 247 filename: string, name of the file in the directory. 248 work_directory: string, path to working directory. 249 source_url: url to download from if file doesn't exist. 250 251 Returns: 252 Path to resulting file. 253 """ 254 if not gfile.Exists(work_directory): 255 gfile.MakeDirs(work_directory) 256 filepath = os.path.join(work_directory, filename) 257 if not gfile.Exists(filepath): 258 urllib.request.urlretrieve(source_url, filepath) 259 with gfile.GFile(filepath) as f: 260 size = f.size() 261 print('Successfully downloaded', filename, size, 'bytes.') 262 return filepath 263 264 265@deprecated(None, 'Please use alternatives such as:' 266 ' tensorflow_datasets.load(\'mnist\')') 267def read_data_sets(train_dir, 268 fake_data=False, 269 one_hot=False, 270 dtype=dtypes.float32, 271 reshape=True, 272 validation_size=5000, 273 seed=None, 274 source_url=DEFAULT_SOURCE_URL): 275 if fake_data: 276 277 def fake(): 278 return _DataSet([], [], 279 fake_data=True, 280 one_hot=one_hot, 281 dtype=dtype, 282 seed=seed) 283 284 train = fake() 285 validation = fake() 286 test = fake() 287 return _Datasets(train=train, validation=validation, test=test) 288 289 if not source_url: # empty string check 290 source_url = DEFAULT_SOURCE_URL 291 292 train_images_file = 'train-images-idx3-ubyte.gz' 293 train_labels_file = 'train-labels-idx1-ubyte.gz' 294 test_images_file = 't10k-images-idx3-ubyte.gz' 295 test_labels_file = 't10k-labels-idx1-ubyte.gz' 296 297 local_file = _maybe_download(train_images_file, train_dir, 298 source_url + train_images_file) 299 with gfile.Open(local_file, 'rb') as f: 300 train_images = _extract_images(f) 301 302 local_file = _maybe_download(train_labels_file, train_dir, 303 source_url + train_labels_file) 304 with gfile.Open(local_file, 'rb') as f: 305 train_labels = _extract_labels(f, one_hot=one_hot) 306 307 local_file = _maybe_download(test_images_file, train_dir, 308 source_url + test_images_file) 309 with gfile.Open(local_file, 'rb') as f: 310 test_images = _extract_images(f) 311 312 local_file = _maybe_download(test_labels_file, train_dir, 313 source_url + test_labels_file) 314 with gfile.Open(local_file, 'rb') as f: 315 test_labels = _extract_labels(f, one_hot=one_hot) 316 317 if not 0 <= validation_size <= len(train_images): 318 raise ValueError( 319 'Validation size should be between 0 and {}. Received: {}.'.format( 320 len(train_images), validation_size)) 321 322 validation_images = train_images[:validation_size] 323 validation_labels = train_labels[:validation_size] 324 train_images = train_images[validation_size:] 325 train_labels = train_labels[validation_size:] 326 327 options = dict(dtype=dtype, reshape=reshape, seed=seed) 328 329 train = _DataSet(train_images, train_labels, **options) 330 validation = _DataSet(validation_images, validation_labels, **options) 331 test = _DataSet(test_images, test_labels, **options) 332 333 return _Datasets(train=train, validation=validation, test=test) 334