dcgan64_input.py

# Copyright 2017 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.
# ==============================================================================

"""CIFAR example using input pipelines."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import flags
import numpy as np
from PIL import Image
import tensorflow as tf

FLAGS = flags.FLAGS

flags.DEFINE_string('train_data_file_64', '',
                    'Path to CIFAR10 training data.')
flags.DEFINE_string('test_data_file_64', '', 'Path to CIFAR10 test data.')

image_size = 64


def parser(serialized_example):
    """Parses a single tf.Example into image and label tensors."""
    features = tf.parse_single_example(
        serialized_example,
        features={
            'image': tf.FixedLenFeature([], tf.string),
            'label': tf.FixedLenFeature([], tf.int64),
        })
    image = tf.decode_raw(features['image'], tf.uint8)
    image.set_shape([image_size*image_size*3])
    # Normalize the values of the image from the range [0, 255] to [-1.0, 1.0]
    image = tf.cast(image, tf.float32) * (2.0 / 255) - 1.0
#   image = tf.transpose(tf.reshape(image, [3, 32*32])) Not needed as ourimage is already in shape image_size * image_size * 3
    label = tf.cast(features['label'], tf.int32)
    return image, label


class InputFunction(object):
    """Wrapper class that is passed as callable to Estimator."""

    def __init__(self, is_training, noise_dim):
        self.is_training = is_training
        self.noise_dim = noise_dim
        self.data_file = (FLAGS.train_data_file_64 if is_training
                          else FLAGS.test_data_file_64)

    def __call__(self, params):
        batch_size = params['batch_size']
        dataset = tf.data.TFRecordDataset([self.data_file])
        dataset = dataset.map(parser, num_parallel_calls=batch_size)
        dataset = dataset.prefetch(4 * batch_size).cache().repeat()
        dataset = dataset.batch(batch_size, drop_remainder=True)
        dataset = dataset.prefetch(2)
        images, labels = dataset.make_one_shot_iterator().get_next()

        # Reshape to give inputs statically known shapes.
        images = tf.reshape(
            images, [batch_size, image_size, image_size, 3])

        random_noise = tf.random_normal([batch_size, self.noise_dim])

        features = {
            'real_images': images,
            'random_noise': random_noise}

        return features, labels


def convert_array_to_image(array):
    """Converts a numpy array to a PIL Image and undoes any rescaling."""
    img = Image.fromarray(np.uint8((array + 1.0) / 2.0 * 255), mode='RGB')
    return img