import numpy as np
import tensorflow as tf
import os

def get_inputs(split, config):
    split_dir = config['split_dir']
    data_dir = config['data_dir']
    dataset = config['dataset']

    split_file = os.path.join(split_dir, dataset, split + '.lst')
    filename_queue = get_filename_queue(split_file, os.path.join(data_dir, dataset))

    if dataset == 'mnist':
        image = get_inputs_mnist(filename_queue, config)
        config['output_size'] = 28
        config['c_dim'] = 1
    elif dataset == "cifar-10":
        image = get_inputs_cifar10(filename_queue, config)
        config['output_size'] = 32
        config['c_dim'] = 3
    else:
        image = get_inputs_image(filename_queue, config)

    image_batch = create_batch([image], config['batch_size'])

    return image_batch

def get_inputs_image(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']

    # Read a record, getting filenames from the filename_queue.
    reader = tf.WholeFileReader()
    key, value = reader.read(filename_queue)
    image = tf.image.decode_image(value, channels=c_dim)
    image = tf.cast(image, tf.float32)/255.

    image_shape = tf.shape(image)
    image_height, image_width = image_shape[0], image_shape[1]
    offset_height = tf.cast((image_height - image_size)/2, tf.int32)
    offset_width = tf.cast((image_width - image_size)/2, tf.int32)
    image = tf.image.crop_to_bounding_box(image, offset_height, offset_width, image_size, image_size)
    image = tf.image.resize_images(image, [output_size, output_size])

    image.set_shape([output_size, output_size, c_dim])

    return image

def get_inputs_mnist(filename_queue, config):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
        # Defaults are not specified since all keys are required.
        features={
            'height': tf.FixedLenFeature([], tf.int64),
            'width': tf.FixedLenFeature([], tf.int64),
            'depth': tf.FixedLenFeature([], tf.int64),
            'label': tf.FixedLenFeature([], tf.int64),
            'image_raw': tf.FixedLenFeature([], tf.string),
        })

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image.set_shape([784])
    image = tf.reshape(image, [28, 28, 1])
    image = tf.cast(image, tf.float32) / 255.

    # Convert label from a scalar uint8 tensor to an int32 scalar.
    label = tf.cast(features['label'], tf.int32)

    binary_image = (tf.random_uniform(image.get_shape()) <= image)
    binary_image = tf.cast(binary_image, tf.float32)
    return binary_image

def get_inputs_cifar10(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']

    # Dimensions of the images in the CIFAR-10 dataset.
    # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
    # input format.
    label_bytes = 1 # 2 for CIFAR-100
    image_bytes = 32 * 32 * 3
    # Every record consists of a label followed by the image, with a
    # fixed number of bytes for each.
    record_bytes = label_bytes + image_bytes

    # Read a record, getting filenames from the filename_queue.
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    key, value = reader.read(filename_queue)
    record = tf.decode_raw(value, tf.uint8)

    # The first bytes represent the label, which we convert from uint8->int32.
    label = tf.cast(record[0], tf.int32)

    # The remaining bytes after the label represent the image, which we reshape
    # from [depth * height * width] to [depth, height, width].
    #tf.strided_slice(record, [label_bytes], [label_bytes + image_bytes])
    image = tf.reshape(record[label_bytes:label_bytes+image_bytes], [3, 32, 32])
    image = tf.cast(image, tf.float32)/255.
    # Convert from [depth, height, width] to [height, width, depth].
    image = tf.transpose(image, [1, 2, 0])

    return image

def get_filename_queue(split_file, data_dir):
    with open(split_file, 'r') as f:
        filenames = f.readlines()
    filenames = [os.path.join(data_dir, f.strip()) for f in filenames]

    for f in filenames:
        if not os.path.exists(f):
            raise ValueError('Failed to find file: ' + f)
    filename_queue = tf.train.string_input_producer(filenames)

    return filename_queue


def create_batch(inputs, batch_size=64, min_queue_examples=1000, num_preprocess_threads=12, enqueue_many=False):
    # Generate a batch of images and labels by building up a queue of examples.
    batch = tf.train.shuffle_batch(
        inputs,
        batch_size=batch_size,
        num_threads=num_preprocess_threads,
        capacity=min_queue_examples + 3 * batch_size,
        min_after_dequeue=min_queue_examples,
        enqueue_many=enqueue_many,
    )
    return batch

b38323a281343f12e9fcf100341503b6789fca35
<mask token>


def get_inputs(split, config):
    split_dir = config['split_dir']
    data_dir = config['data_dir']
    dataset = config['dataset']
    split_file = os.path.join(split_dir, dataset, split + '.lst')
    filename_queue = get_filename_queue(split_file, os.path.join(data_dir,
        dataset))
    if dataset == 'mnist':
        image = get_inputs_mnist(filename_queue, config)
        config['output_size'] = 28
        config['c_dim'] = 1
    elif dataset == 'cifar-10':
        image = get_inputs_cifar10(filename_queue, config)
        config['output_size'] = 32
        config['c_dim'] = 3
    else:
        image = get_inputs_image(filename_queue, config)
    image_batch = create_batch([image], config['batch_size'])
    return image_batch


<mask token>


def get_inputs_cifar10(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']
    label_bytes = 1
    image_bytes = 32 * 32 * 3
    record_bytes = label_bytes + image_bytes
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    key, value = reader.read(filename_queue)
    record = tf.decode_raw(value, tf.uint8)
    label = tf.cast(record[0], tf.int32)
    image = tf.reshape(record[label_bytes:label_bytes + image_bytes], [3, 
        32, 32])
    image = tf.cast(image, tf.float32) / 255.0
    image = tf.transpose(image, [1, 2, 0])
    return image


def get_filename_queue(split_file, data_dir):
    with open(split_file, 'r') as f:
        filenames = f.readlines()
    filenames = [os.path.join(data_dir, f.strip()) for f in filenames]
    for f in filenames:
        if not os.path.exists(f):
            raise ValueError('Failed to find file: ' + f)
    filename_queue = tf.train.string_input_producer(filenames)
    return filename_queue


def create_batch(inputs, batch_size=64, min_queue_examples=1000,
    num_preprocess_threads=12, enqueue_many=False):
    batch = tf.train.shuffle_batch(inputs, batch_size=batch_size,
        num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 *
        batch_size, min_after_dequeue=min_queue_examples, enqueue_many=
        enqueue_many)
    return batch

<mask token>


def get_inputs(split, config):
    split_dir = config['split_dir']
    data_dir = config['data_dir']
    dataset = config['dataset']
    split_file = os.path.join(split_dir, dataset, split + '.lst')
    filename_queue = get_filename_queue(split_file, os.path.join(data_dir,
        dataset))
    if dataset == 'mnist':
        image = get_inputs_mnist(filename_queue, config)
        config['output_size'] = 28
        config['c_dim'] = 1
    elif dataset == 'cifar-10':
        image = get_inputs_cifar10(filename_queue, config)
        config['output_size'] = 32
        config['c_dim'] = 3
    else:
        image = get_inputs_image(filename_queue, config)
    image_batch = create_batch([image], config['batch_size'])
    return image_batch


def get_inputs_image(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']
    reader = tf.WholeFileReader()
    key, value = reader.read(filename_queue)
    image = tf.image.decode_image(value, channels=c_dim)
    image = tf.cast(image, tf.float32) / 255.0
    image_shape = tf.shape(image)
    image_height, image_width = image_shape[0], image_shape[1]
    offset_height = tf.cast((image_height - image_size) / 2, tf.int32)
    offset_width = tf.cast((image_width - image_size) / 2, tf.int32)
    image = tf.image.crop_to_bounding_box(image, offset_height,
        offset_width, image_size, image_size)
    image = tf.image.resize_images(image, [output_size, output_size])
    image.set_shape([output_size, output_size, c_dim])
    return image


<mask token>


def get_inputs_cifar10(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']
    label_bytes = 1
    image_bytes = 32 * 32 * 3
    record_bytes = label_bytes + image_bytes
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    key, value = reader.read(filename_queue)
    record = tf.decode_raw(value, tf.uint8)
    label = tf.cast(record[0], tf.int32)
    image = tf.reshape(record[label_bytes:label_bytes + image_bytes], [3, 
        32, 32])
    image = tf.cast(image, tf.float32) / 255.0
    image = tf.transpose(image, [1, 2, 0])
    return image


def get_filename_queue(split_file, data_dir):
    with open(split_file, 'r') as f:
        filenames = f.readlines()
    filenames = [os.path.join(data_dir, f.strip()) for f in filenames]
    for f in filenames:
        if not os.path.exists(f):
            raise ValueError('Failed to find file: ' + f)
    filename_queue = tf.train.string_input_producer(filenames)
    return filename_queue


def create_batch(inputs, batch_size=64, min_queue_examples=1000,
    num_preprocess_threads=12, enqueue_many=False):
    batch = tf.train.shuffle_batch(inputs, batch_size=batch_size,
        num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 *
        batch_size, min_after_dequeue=min_queue_examples, enqueue_many=
        enqueue_many)
    return batch

<mask token>


def get_inputs(split, config):
    split_dir = config['split_dir']
    data_dir = config['data_dir']
    dataset = config['dataset']
    split_file = os.path.join(split_dir, dataset, split + '.lst')
    filename_queue = get_filename_queue(split_file, os.path.join(data_dir,
        dataset))
    if dataset == 'mnist':
        image = get_inputs_mnist(filename_queue, config)
        config['output_size'] = 28
        config['c_dim'] = 1
    elif dataset == 'cifar-10':
        image = get_inputs_cifar10(filename_queue, config)
        config['output_size'] = 32
        config['c_dim'] = 3
    else:
        image = get_inputs_image(filename_queue, config)
    image_batch = create_batch([image], config['batch_size'])
    return image_batch


def get_inputs_image(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']
    reader = tf.WholeFileReader()
    key, value = reader.read(filename_queue)
    image = tf.image.decode_image(value, channels=c_dim)
    image = tf.cast(image, tf.float32) / 255.0
    image_shape = tf.shape(image)
    image_height, image_width = image_shape[0], image_shape[1]
    offset_height = tf.cast((image_height - image_size) / 2, tf.int32)
    offset_width = tf.cast((image_width - image_size) / 2, tf.int32)
    image = tf.image.crop_to_bounding_box(image, offset_height,
        offset_width, image_size, image_size)
    image = tf.image.resize_images(image, [output_size, output_size])
    image.set_shape([output_size, output_size, c_dim])
    return image


def get_inputs_mnist(filename_queue, config):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(serialized_example, features={
        'height': tf.FixedLenFeature([], tf.int64), 'width': tf.
        FixedLenFeature([], tf.int64), 'depth': tf.FixedLenFeature([], tf.
        int64), 'label': tf.FixedLenFeature([], tf.int64), 'image_raw': tf.
        FixedLenFeature([], tf.string)})
    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image.set_shape([784])
    image = tf.reshape(image, [28, 28, 1])
    image = tf.cast(image, tf.float32) / 255.0
    label = tf.cast(features['label'], tf.int32)
    binary_image = tf.random_uniform(image.get_shape()) <= image
    binary_image = tf.cast(binary_image, tf.float32)
    return binary_image


def get_inputs_cifar10(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']
    label_bytes = 1
    image_bytes = 32 * 32 * 3
    record_bytes = label_bytes + image_bytes
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    key, value = reader.read(filename_queue)
    record = tf.decode_raw(value, tf.uint8)
    label = tf.cast(record[0], tf.int32)
    image = tf.reshape(record[label_bytes:label_bytes + image_bytes], [3, 
        32, 32])
    image = tf.cast(image, tf.float32) / 255.0
    image = tf.transpose(image, [1, 2, 0])
    return image


def get_filename_queue(split_file, data_dir):
    with open(split_file, 'r') as f:
        filenames = f.readlines()
    filenames = [os.path.join(data_dir, f.strip()) for f in filenames]
    for f in filenames:
        if not os.path.exists(f):
            raise ValueError('Failed to find file: ' + f)
    filename_queue = tf.train.string_input_producer(filenames)
    return filename_queue


def create_batch(inputs, batch_size=64, min_queue_examples=1000,
    num_preprocess_threads=12, enqueue_many=False):
    batch = tf.train.shuffle_batch(inputs, batch_size=batch_size,
        num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 *
        batch_size, min_after_dequeue=min_queue_examples, enqueue_many=
        enqueue_many)
    return batch

import numpy as np
import tensorflow as tf
import os


def get_inputs(split, config):
    split_dir = config['split_dir']
    data_dir = config['data_dir']
    dataset = config['dataset']
    split_file = os.path.join(split_dir, dataset, split + '.lst')
    filename_queue = get_filename_queue(split_file, os.path.join(data_dir,
        dataset))
    if dataset == 'mnist':
        image = get_inputs_mnist(filename_queue, config)
        config['output_size'] = 28
        config['c_dim'] = 1
    elif dataset == 'cifar-10':
        image = get_inputs_cifar10(filename_queue, config)
        config['output_size'] = 32
        config['c_dim'] = 3
    else:
        image = get_inputs_image(filename_queue, config)
    image_batch = create_batch([image], config['batch_size'])
    return image_batch


def get_inputs_image(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']
    reader = tf.WholeFileReader()
    key, value = reader.read(filename_queue)
    image = tf.image.decode_image(value, channels=c_dim)
    image = tf.cast(image, tf.float32) / 255.0
    image_shape = tf.shape(image)
    image_height, image_width = image_shape[0], image_shape[1]
    offset_height = tf.cast((image_height - image_size) / 2, tf.int32)
    offset_width = tf.cast((image_width - image_size) / 2, tf.int32)
    image = tf.image.crop_to_bounding_box(image, offset_height,
        offset_width, image_size, image_size)
    image = tf.image.resize_images(image, [output_size, output_size])
    image.set_shape([output_size, output_size, c_dim])
    return image


def get_inputs_mnist(filename_queue, config):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(serialized_example, features={
        'height': tf.FixedLenFeature([], tf.int64), 'width': tf.
        FixedLenFeature([], tf.int64), 'depth': tf.FixedLenFeature([], tf.
        int64), 'label': tf.FixedLenFeature([], tf.int64), 'image_raw': tf.
        FixedLenFeature([], tf.string)})
    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image.set_shape([784])
    image = tf.reshape(image, [28, 28, 1])
    image = tf.cast(image, tf.float32) / 255.0
    label = tf.cast(features['label'], tf.int32)
    binary_image = tf.random_uniform(image.get_shape()) <= image
    binary_image = tf.cast(binary_image, tf.float32)
    return binary_image


def get_inputs_cifar10(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']
    label_bytes = 1
    image_bytes = 32 * 32 * 3
    record_bytes = label_bytes + image_bytes
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    key, value = reader.read(filename_queue)
    record = tf.decode_raw(value, tf.uint8)
    label = tf.cast(record[0], tf.int32)
    image = tf.reshape(record[label_bytes:label_bytes + image_bytes], [3, 
        32, 32])
    image = tf.cast(image, tf.float32) / 255.0
    image = tf.transpose(image, [1, 2, 0])
    return image


def get_filename_queue(split_file, data_dir):
    with open(split_file, 'r') as f:
        filenames = f.readlines()
    filenames = [os.path.join(data_dir, f.strip()) for f in filenames]
    for f in filenames:
        if not os.path.exists(f):
            raise ValueError('Failed to find file: ' + f)
    filename_queue = tf.train.string_input_producer(filenames)
    return filename_queue


def create_batch(inputs, batch_size=64, min_queue_examples=1000,
    num_preprocess_threads=12, enqueue_many=False):
    batch = tf.train.shuffle_batch(inputs, batch_size=batch_size,
        num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 *
        batch_size, min_after_dequeue=min_queue_examples, enqueue_many=
        enqueue_many)
    return batch

import numpy as np
import tensorflow as tf
import os

def get_inputs(split, config):
    split_dir = config['split_dir']
    data_dir = config['data_dir']
    dataset = config['dataset']

    split_file = os.path.join(split_dir, dataset, split + '.lst')
    filename_queue = get_filename_queue(split_file, os.path.join(data_dir, dataset))

    if dataset == 'mnist':
        image = get_inputs_mnist(filename_queue, config)
        config['output_size'] = 28
        config['c_dim'] = 1
    elif dataset == "cifar-10":
        image = get_inputs_cifar10(filename_queue, config)
        config['output_size'] = 32
        config['c_dim'] = 3
    else:
        image = get_inputs_image(filename_queue, config)

    image_batch = create_batch([image], config['batch_size'])

    return image_batch

def get_inputs_image(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']

    # Read a record, getting filenames from the filename_queue.
    reader = tf.WholeFileReader()
    key, value = reader.read(filename_queue)
    image = tf.image.decode_image(value, channels=c_dim)
    image = tf.cast(image, tf.float32)/255.

    image_shape = tf.shape(image)
    image_height, image_width = image_shape[0], image_shape[1]
    offset_height = tf.cast((image_height - image_size)/2, tf.int32)
    offset_width = tf.cast((image_width - image_size)/2, tf.int32)
    image = tf.image.crop_to_bounding_box(image, offset_height, offset_width, image_size, image_size)
    image = tf.image.resize_images(image, [output_size, output_size])

    image.set_shape([output_size, output_size, c_dim])

    return image

def get_inputs_mnist(filename_queue, config):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
        # Defaults are not specified since all keys are required.
        features={
            'height': tf.FixedLenFeature([], tf.int64),
            'width': tf.FixedLenFeature([], tf.int64),
            'depth': tf.FixedLenFeature([], tf.int64),
            'label': tf.FixedLenFeature([], tf.int64),
            'image_raw': tf.FixedLenFeature([], tf.string),
        })

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image.set_shape([784])
    image = tf.reshape(image, [28, 28, 1])
    image = tf.cast(image, tf.float32) / 255.

    # Convert label from a scalar uint8 tensor to an int32 scalar.
    label = tf.cast(features['label'], tf.int32)

    binary_image = (tf.random_uniform(image.get_shape()) <= image)
    binary_image = tf.cast(binary_image, tf.float32)
    return binary_image

def get_inputs_cifar10(filename_queue, config):
    output_size = config['output_size']
    image_size = config['image_size']
    c_dim = config['c_dim']

    # Dimensions of the images in the CIFAR-10 dataset.
    # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
    # input format.
    label_bytes = 1 # 2 for CIFAR-100
    image_bytes = 32 * 32 * 3
    # Every record consists of a label followed by the image, with a
    # fixed number of bytes for each.
    record_bytes = label_bytes + image_bytes

    # Read a record, getting filenames from the filename_queue.
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    key, value = reader.read(filename_queue)
    record = tf.decode_raw(value, tf.uint8)

    # The first bytes represent the label, which we convert from uint8->int32.
    label = tf.cast(record[0], tf.int32)

    # The remaining bytes after the label represent the image, which we reshape
    # from [depth * height * width] to [depth, height, width].
    #tf.strided_slice(record, [label_bytes], [label_bytes + image_bytes])
    image = tf.reshape(record[label_bytes:label_bytes+image_bytes], [3, 32, 32])
    image = tf.cast(image, tf.float32)/255.
    # Convert from [depth, height, width] to [height, width, depth].
    image = tf.transpose(image, [1, 2, 0])

    return image

def get_filename_queue(split_file, data_dir):
    with open(split_file, 'r') as f:
        filenames = f.readlines()
    filenames = [os.path.join(data_dir, f.strip()) for f in filenames]

    for f in filenames:
        if not os.path.exists(f):
            raise ValueError('Failed to find file: ' + f)
    filename_queue = tf.train.string_input_producer(filenames)

    return filename_queue


def create_batch(inputs, batch_size=64, min_queue_examples=1000, num_preprocess_threads=12, enqueue_many=False):
    # Generate a batch of images and labels by building up a queue of examples.
    batch = tf.train.shuffle_batch(
        inputs,
        batch_size=batch_size,
        num_threads=num_preprocess_threads,
        capacity=min_queue_examples + 3 * batch_size,
        min_after_dequeue=min_queue_examples,
        enqueue_many=enqueue_many,
    )
    return batch
