input_data.py

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data (deprecated).

This module and all its submodules are deprecated.
"""


import collections
import gzip
import os

import numpy
from six.moves import xrange  # pylint: disable=redefined-builtin
from six.moves import urllib
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_Datasets = collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"


def _read32(bytestream):
    dt = numpy.dtype(numpy.uint32).newbyteorder(">")
    return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]


@deprecated(None, "Please use tf.data to implement this functionality.")
def _extract_images(f):
    """Extract the images into a 4D uint8 numpy array [index, y, x, depth].

    Args:
      f: A file object that can be passed into a gzip reader.

    Returns:
      data: A 4D uint8 numpy array [index, y, x, depth].

    Raises:
      ValueError: If the bytestream does not start with 2051.

    """
    print("Extracting", f.name)
    with gzip.GzipFile(fileobj=f) as bytestream:
        magic = _read32(bytestream)
        if magic != 2051:
            raise ValueError(
                "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)
            )
        num_images = _read32(bytestream)
        rows = _read32(bytestream)
        cols = _read32(bytestream)
        buf = bytestream.read(rows * cols * num_images)
        data = numpy.frombuffer(buf, dtype=numpy.uint8)
        data = data.reshape(num_images, rows, cols, 1)
        return data


@deprecated(None, "Please use tf.one_hot on tensors.")
def _dense_to_one_hot(labels_dense, num_classes):
    """Convert class labels from scalars to one-hot vectors."""
    num_labels = labels_dense.shape[0]
    index_offset = numpy.arange(num_labels) * num_classes
    labels_one_hot = numpy.zeros((num_labels, num_classes))
    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
    return labels_one_hot


@deprecated(None, "Please use tf.data to implement this functionality.")
def _extract_labels(f, one_hot=False, num_classes=10):
    """Extract the labels into a 1D uint8 numpy array [index].

    Args:
      f: A file object that can be passed into a gzip reader.
      one_hot: Does one hot encoding for the result.
      num_classes: Number of classes for the one hot encoding.

    Returns:
      labels: a 1D uint8 numpy array.

    Raises:
      ValueError: If the bystream doesn't start with 2049.
    """
    print("Extracting", f.name)
    with gzip.GzipFile(fileobj=f) as bytestream:
        magic = _read32(bytestream)
        if magic != 2049:
            raise ValueError(
                "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)
            )
        num_items = _read32(bytestream)
        buf = bytestream.read(num_items)
        labels = numpy.frombuffer(buf, dtype=numpy.uint8)
        if one_hot:
            return _dense_to_one_hot(labels, num_classes)
        return labels


class _DataSet:
    """Container class for a _DataSet (deprecated).

    THIS CLASS IS DEPRECATED.
    """

    @deprecated(
        None,
        "Please use alternatives such as official/mnist/_DataSet.py"
        " from tensorflow/models.",
    )
    def __init__(
        self,
        images,
        labels,
        fake_data=False,
        one_hot=False,
        dtype=dtypes.float32,
        reshape=True,
        seed=None,
    ):
        """Construct a _DataSet.

        one_hot arg is used only if fake_data is true.  `dtype` can be either
        `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
        `[0, 1]`.  Seed arg provides for convenient deterministic testing.

        Args:
          images: The images
          labels: The labels
          fake_data: Ignore inages and labels, use fake data.
          one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
            False).
          dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
            range [0,255]. float32 output has range [0,1].
          reshape: Bool. If True returned images are returned flattened to vectors.
          seed: The random seed to use.
        """
        seed1, seed2 = random_seed.get_seed(seed)
        # If op level seed is not set, use whatever graph level seed is returned
        numpy.random.seed(seed1 if seed is None else seed2)
        dtype = dtypes.as_dtype(dtype).base_dtype
        if dtype not in (dtypes.uint8, dtypes.float32):
            raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
        if fake_data:
            self._num_examples = 10000
            self.one_hot = one_hot
        else:
            assert (
                images.shape[0] == labels.shape[0]
            ), f"images.shape: {images.shape} labels.shape: {labels.shape}"
            self._num_examples = images.shape[0]

            # Convert shape from [num examples, rows, columns, depth]
            # to [num examples, rows*columns] (assuming depth == 1)
            if reshape:
                assert images.shape[3] == 1
                images = images.reshape(
                    images.shape[0], images.shape[1] * images.shape[2]
                )
            if dtype == dtypes.float32:
                # Convert from [0, 255] -> [0.0, 1.0].
                images = images.astype(numpy.float32)
                images = numpy.multiply(images, 1.0 / 255.0)
        self._images = images
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0

    @property
    def images(self):
        return self._images

    @property
    def labels(self):
        return self._labels

    @property
    def num_examples(self):
        return self._num_examples

    @property
    def epochs_completed(self):
        return self._epochs_completed

    def next_batch(self, batch_size, fake_data=False, shuffle=True):
        """Return the next `batch_size` examples from this data set."""
        if fake_data:
            fake_image = [1] * 784
            if self.one_hot:
                fake_label = [1] + [0] * 9
            else:
                fake_label = 0
            return (
                [fake_image for _ in xrange(batch_size)],
                [fake_label for _ in xrange(batch_size)],
            )
        start = self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
            perm0 = numpy.arange(self._num_examples)
            numpy.random.shuffle(perm0)
            self._images = self.images[perm0]
            self._labels = self.labels[perm0]
        # Go to the next epoch
        if start + batch_size > self._num_examples:
            # Finished epoch
            self._epochs_completed += 1
            # Get the rest examples in this epoch
            rest_num_examples = self._num_examples - start
            images_rest_part = self._images[start : self._num_examples]
            labels_rest_part = self._labels[start : self._num_examples]
            # Shuffle the data
            if shuffle:
                perm = numpy.arange(self._num_examples)
                numpy.random.shuffle(perm)
                self._images = self.images[perm]
                self._labels = self.labels[perm]
            # Start next epoch
            start = 0
            self._index_in_epoch = batch_size - rest_num_examples
            end = self._index_in_epoch
            images_new_part = self._images[start:end]
            labels_new_part = self._labels[start:end]
            return (
                numpy.concatenate((images_rest_part, images_new_part), axis=0),
                numpy.concatenate((labels_rest_part, labels_new_part), axis=0),
            )
        else:
            self._index_in_epoch += batch_size
            end = self._index_in_epoch
            return self._images[start:end], self._labels[start:end]


@deprecated(None, "Please write your own downloading logic.")
def _maybe_download(filename, work_directory, source_url):
    """Download the data from source url, unless it's already here.

    Args:
        filename: string, name of the file in the directory.
        work_directory: string, path to working directory.
        source_url: url to download from if file doesn't exist.

    Returns:
        Path to resulting file.
    """
    if not gfile.Exists(work_directory):
        gfile.MakeDirs(work_directory)
    filepath = os.path.join(work_directory, filename)
    if not gfile.Exists(filepath):
        urllib.request.urlretrieve(source_url, filepath)
        with gfile.GFile(filepath) as f:
            size = f.size()
        print("Successfully downloaded", filename, size, "bytes.")
    return filepath


@deprecated(
    None, "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"
)
def read_data_sets(
    train_dir,
    fake_data=False,
    one_hot=False,
    dtype=dtypes.float32,
    reshape=True,
    validation_size=5000,
    seed=None,
    source_url=DEFAULT_SOURCE_URL,
):
    if fake_data:

        def fake():
            return _DataSet(
                [], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed
            )

        train = fake()
        validation = fake()
        test = fake()
        return _Datasets(train=train, validation=validation, test=test)
    if not source_url:  # empty string check
        source_url = DEFAULT_SOURCE_URL
    train_images_file = "train-images-idx3-ubyte.gz"
    train_labels_file = "train-labels-idx1-ubyte.gz"
    test_images_file = "t10k-images-idx3-ubyte.gz"
    test_labels_file = "t10k-labels-idx1-ubyte.gz"

    local_file = _maybe_download(
        train_images_file, train_dir, source_url + train_images_file
    )
    with gfile.Open(local_file, "rb") as f:
        train_images = _extract_images(f)
    local_file = _maybe_download(
        train_labels_file, train_dir, source_url + train_labels_file
    )
    with gfile.Open(local_file, "rb") as f:
        train_labels = _extract_labels(f, one_hot=one_hot)
    local_file = _maybe_download(
        test_images_file, train_dir, source_url + test_images_file
    )
    with gfile.Open(local_file, "rb") as f:
        test_images = _extract_images(f)
    local_file = _maybe_download(
        test_labels_file, train_dir, source_url + test_labels_file
    )
    with gfile.Open(local_file, "rb") as f:
        test_labels = _extract_labels(f, one_hot=one_hot)
    if not 0 <= validation_size <= len(train_images):
        raise ValueError(
            f"Validation size should be between 0 "
            f"and {len(train_images)}. Received: {validation_size}."
        )
    validation_images = train_images[:validation_size]
    validation_labels = train_labels[:validation_size]
    train_images = train_images[validation_size:]
    train_labels = train_labels[validation_size:]

    options = dict(dtype=dtype, reshape=reshape, seed=seed)

    train = _DataSet(train_images, train_labels, **options)
    validation = _DataSet(validation_images, validation_labels, **options)
    test = _DataSet(test_images, test_labels, **options)

    return _Datasets(train=train, validation=validation, test=test)