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3ebfd850e1
* Test examples for pep8 compliance. * Make rl_pong example pep8 compliant. * Make policy gradient example pep8 compliant. * Make lbfgs example pep8 compliant. * Make hyperopt example pep8 compliant. * Make a3c example pep8 compliant. * Make evolution strategies example pep8 compliant. * Make resnet example pep8 compliant. * Fix.
117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
"""CIFAR dataset input module, with the majority taken from
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https://github.com/tensorflow/models/tree/master/resnet.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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def build_data(data_path, size, dataset):
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"""Creates the queue and preprocessing operations for the dataset.
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Args:
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data_path: Filename for cifar10 data.
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size: The number of images in the dataset.
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dataset: The dataset we are using.
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Returns:
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queue: A Tensorflow queue for extracting the images and labels.
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"""
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image_size = 32
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if dataset == "cifar10":
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label_bytes = 1
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label_offset = 0
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elif dataset == "cifar100":
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label_bytes = 1
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label_offset = 1
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depth = 3
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image_bytes = image_size * image_size * depth
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record_bytes = label_bytes + label_offset + image_bytes
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data_files = tf.gfile.Glob(data_path)
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file_queue = tf.train.string_input_producer(data_files, shuffle=True)
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# Read examples from files in the filename queue.
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reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
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_, value = reader.read(file_queue)
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# Convert these examples to dense labels and processed images.
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record = tf.reshape(tf.decode_raw(value, tf.uint8), [record_bytes])
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label = tf.cast(tf.slice(record, [label_offset], [label_bytes]), tf.int32)
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# Convert from string to [depth * height * width] to [depth, height, width].
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depth_major = tf.reshape(tf.slice(record, [label_bytes], [image_bytes]),
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[depth, image_size, image_size])
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# Convert from [depth, height, width] to [height, width, depth].
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image = tf.cast(tf.transpose(depth_major, [1, 2, 0]), tf.float32)
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queue = tf.train.shuffle_batch([image, label], size, size, 0, num_threads=16)
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return queue
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def build_input(data, batch_size, dataset, train):
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"""Build CIFAR image and labels.
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Args:
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data_path: Filename for cifar10 data.
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batch_size: Input batch size.
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train: True if we are training and false if we are testing.
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Returns:
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images: Batches of images of size [batch_size, image_size, image_size, 3].
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labels: Batches of labels of size [batch_size, num_classes].
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Raises:
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ValueError: When the specified dataset is not supported.
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"""
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images_constant = tf.constant(data[0])
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labels_constant = tf.constant(data[1])
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image_size = 32
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depth = 3
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num_classes = 10 if dataset == "cifar10" else 100
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image, label = tf.train.slice_input_producer([images_constant,
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labels_constant],
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capacity=16 * batch_size)
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if train:
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image = tf.image.resize_image_with_crop_or_pad(image, image_size + 4,
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image_size + 4)
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image = tf.random_crop(image, [image_size, image_size, 3])
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image = tf.image.random_flip_left_right(image)
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image = tf.image.per_image_standardization(image)
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example_queue = tf.RandomShuffleQueue(
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capacity=16 * batch_size,
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min_after_dequeue=8 * batch_size,
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dtypes=[tf.float32, tf.int32],
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shapes=[[image_size, image_size, depth], [1]])
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num_threads = 16
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else:
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image = tf.image.resize_image_with_crop_or_pad(image, image_size,
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image_size)
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image = tf.image.per_image_standardization(image)
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example_queue = tf.FIFOQueue(
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3 * batch_size,
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dtypes=[tf.float32, tf.int32],
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shapes=[[image_size, image_size, depth], [1]])
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num_threads = 1
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example_enqueue_op = example_queue.enqueue([image, label])
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tf.train.add_queue_runner(tf.train.queue_runner.QueueRunner(
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example_queue, [example_enqueue_op] * num_threads))
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# Read "batch" labels + images from the example queue.
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images, labels = example_queue.dequeue_many(batch_size)
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labels = tf.reshape(labels, [batch_size, 1])
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indices = tf.reshape(tf.range(0, batch_size, 1), [batch_size, 1])
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labels = tf.sparse_to_dense(
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tf.concat([indices, labels], 1),
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[batch_size, num_classes], 1.0, 0.0)
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assert len(images.get_shape()) == 4
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assert images.get_shape()[0] == batch_size
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assert images.get_shape()[-1] == 3
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assert len(labels.get_shape()) == 2
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assert labels.get_shape()[0] == batch_size
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assert labels.get_shape()[1] == num_classes
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if not train:
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tf.summary.image("images", images)
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return images, labels
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