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Diffstat (limited to 'tensorflow/models/image/cifar10/cifar10.py')
| -rw-r--r-- | tensorflow/models/image/cifar10/cifar10.py | 480 |
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diff --git a/tensorflow/models/image/cifar10/cifar10.py b/tensorflow/models/image/cifar10/cifar10.py new file mode 100644 index 0000000000..7870080820 --- /dev/null +++ b/tensorflow/models/image/cifar10/cifar10.py @@ -0,0 +1,480 @@ +"""Builds the CIFAR-10 network. + +Summary of available functions: + + # Compute input images and labels for training. If you would like to run + # evaluations, use input() instead. + inputs, labels = distorted_inputs() + + # Compute inference on the model inputs to make a prediction. + predictions = inference(inputs) + + # Compute the total loss of the prediction with respect to the labels. + loss = loss(predictions, labels) + + # Create a graph to run one step of training with respect to the loss. + train_op = train(loss, global_step) +""" +# pylint: disable=missing-docstring +import gzip +import os +import re +import sys +import tarfile +import urllib + +import tensorflow.python.platform +import tensorflow as tf + +from tensorflow.models.image.cifar10 import cifar10_input +from tensorflow.python.platform import gfile + +FLAGS = tf.app.flags.FLAGS + +# Basic model parameters. +tf.app.flags.DEFINE_integer('batch_size', 128, + """Number of images to process in a batch.""") +tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar10_data', + """Path to the CIFAR-10 data directory.""") + +# Process images of this size. Note that this differs from the original CIFAR +# image size of 32 x 32. If one alters this number, then the entire model +# architecture will change and any model would need to be retrained. +IMAGE_SIZE = 24 + +# Global constants describing the CIFAR-10 data set. +NUM_CLASSES = 10 +NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000 +NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000 + +# Constants describing the training process. +MOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average. +NUM_EPOCHS_PER_DECAY = 350.0 # Epochs after which learning rate decays. +LEARNING_RATE_DECAY_FACTOR = 0.1 # Learning rate decay factor. +INITIAL_LEARNING_RATE = 0.1 # Initial learning rate. + +# If a model is trained with multiple GPU's prefix all Op names with tower_name +# to differentiate the operations. Note that this prefix is removed from the +# names of the summaries when visualizing a model. +TOWER_NAME = 'tower' + +DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz' + + +def _activation_summary(x): + """Helper to create summaries for activations. + + Creates a summary that provides a histogram of activations. + Creates a summary that measure the sparsity of activations. + + Args: + x: Tensor + Returns: + nothing + """ + # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training + # session. This helps the clarity of presentation on tensorboard. + tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) + tf.histogram_summary(tensor_name + '/activations', x) + tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) + + +def _variable_on_cpu(name, shape, initializer): + """Helper to create a Variable stored on CPU memory. + + Args: + name: name of the variable + shape: list of ints + initializer: initializer for Variable + + Returns: + Variable Tensor + """ + with tf.device('/cpu:0'): + var = tf.get_variable(name, shape, initializer=initializer) + return var + + +def _variable_with_weight_decay(name, shape, stddev, wd): + """Helper to create an initialized Variable with weight decay. + + Note that the Variable is initialized with a truncated normal distribution. + A weight decay is added only if one is specified. + + Args: + name: name of the variable + shape: list of ints + stddev: standard deviation of a truncated Gaussian + wd: add L2Loss weight decay multiplied by this float. If None, weight + decay is not added for this Variable. + + Returns: + Variable Tensor + """ + var = _variable_on_cpu(name, shape, + tf.truncated_normal_initializer(stddev=stddev)) + if wd: + weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + return var + + +def _generate_image_and_label_batch(image, label, min_queue_examples): + """Construct a queued batch of images and labels. + + Args: + image: 3-D Tensor of [IMAGE_SIZE, IMAGE_SIZE, 3] of type.float32. + label: 1-D Tensor of type.int32 + min_queue_examples: int32, minimum number of samples to retain + in the queue that provides of batches of examples. + + Returns: + images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. + labels: Labels. 1D tensor of [batch_size] size. + """ + # Create a queue that shuffles the examples, and then + # read 'FLAGS.batch_size' images + labels from the example queue. + num_preprocess_threads = 16 + images, label_batch = tf.train.shuffle_batch( + [image, label], + batch_size=FLAGS.batch_size, + num_threads=num_preprocess_threads, + capacity=min_queue_examples + 3 * FLAGS.batch_size, + min_after_dequeue=min_queue_examples) + + # Display the training images in the visualizer. + tf.image_summary('images', images) + + return images, tf.reshape(label_batch, [FLAGS.batch_size]) + + +def distorted_inputs(): + """Construct distorted input for CIFAR training using the Reader ops. + + Raises: + ValueError: if no data_dir + + Returns: + images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. + labels: Labels. 1D tensor of [batch_size] size. + """ + filenames = [os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin', + 'data_batch_%d.bin' % i) + for i in xrange(1, 5)] + for f in filenames: + if not gfile.Exists(f): + raise ValueError('Failed to find file: ' + f) + + # Create a queue that produces the filenames to read. + filename_queue = tf.train.string_input_producer(filenames) + + # Read examples from files in the filename queue. + read_input = cifar10_input.read_cifar10(filename_queue) + reshaped_image = tf.cast(read_input.uint8image, tf.float32) + + height = IMAGE_SIZE + width = IMAGE_SIZE + + # Image processing for training the network. Note the many random + # distortions applied to the image. + + # Randomly crop a [height, width] section of the image. + distorted_image = tf.image.random_crop(reshaped_image, [height, width]) + + # Randomly flip the image horizontally. + distorted_image = tf.image.random_flip_left_right(distorted_image) + + # Because these operations are not commutative, consider randomizing + # randomize the order their operation. + distorted_image = tf.image.random_brightness(distorted_image, + max_delta=63) + distorted_image = tf.image.random_contrast(distorted_image, + lower=0.2, upper=1.8) + + # Subtract off the mean and divide by the variance of the pixels. + float_image = tf.image.per_image_whitening(distorted_image) + + # Ensure that the random shuffling has good mixing properties. + min_fraction_of_examples_in_queue = 0.4 + min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * + min_fraction_of_examples_in_queue) + print ('Filling queue with %d CIFAR images before starting to train. ' + 'This will take a few minutes.' % min_queue_examples) + + # Generate a batch of images and labels by building up a queue of examples. + return _generate_image_and_label_batch(float_image, read_input.label, + min_queue_examples) + + +def inputs(eval_data): + """Construct input for CIFAR evaluation using the Reader ops. + + Args: + eval_data: bool, indicating if one should use the train or eval data set. + + Raises: + ValueError: if no data_dir + + Returns: + images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. + labels: Labels. 1D tensor of [batch_size] size. + """ + if not FLAGS.data_dir: + raise ValueError('Please supply a data_dir') + + if not eval_data: + filenames = [os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin', + 'data_batch_%d.bin' % i) + for i in xrange(1, 5)] + num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN + else: + filenames = [os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin', + 'test_batch.bin')] + num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL + + for f in filenames: + if not gfile.Exists(f): + raise ValueError('Failed to find file: ' + f) + + # Create a queue that produces the filenames to read. + filename_queue = tf.train.string_input_producer(filenames) + + # Read examples from files in the filename queue. + read_input = cifar10_input.read_cifar10(filename_queue) + reshaped_image = tf.cast(read_input.uint8image, tf.float32) + + height = IMAGE_SIZE + width = IMAGE_SIZE + + # Image processing for evaluation. + # Crop the central [height, width] of the image. + resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, + width, height) + + # Subtract off the mean and divide by the variance of the pixels. + float_image = tf.image.per_image_whitening(resized_image) + + # Ensure that the random shuffling has good mixing properties. + min_fraction_of_examples_in_queue = 0.4 + min_queue_examples = int(num_examples_per_epoch * + min_fraction_of_examples_in_queue) + + # Generate a batch of images and labels by building up a queue of examples. + return _generate_image_and_label_batch(float_image, read_input.label, + min_queue_examples) + + +def inference(images): + """Build the CIFAR-10 model. + + Args: + images: Images returned from distorted_inputs() or inputs(). + + Returns: + Logits. + """ + # We instantiate all variables using tf.get_variable() instead of + # tf.Variable() in order to share variables across multiple GPU training runs. + # If we only ran this model on a single GPU, we could simplify this function + # by replacing all instances of tf.get_variable() with tf.Variable(). + # + # conv1 + with tf.variable_scope('conv1') as scope: + kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64], + stddev=1e-4, wd=0.0) + conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') + biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0)) + bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape().as_list()) + conv1 = tf.nn.relu(bias, name=scope.name) + _activation_summary(conv1) + + # pool1 + pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], + padding='SAME', name='pool1') + # norm1 + norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, + name='norm1') + + # conv2 + with tf.variable_scope('conv2') as scope: + kernel = _variable_with_weight_decay('weights', shape=[5, 5, 64, 64], + stddev=1e-4, wd=0.0) + conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME') + biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1)) + bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape().as_list()) + conv2 = tf.nn.relu(bias, name=scope.name) + _activation_summary(conv2) + + # norm2 + norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, + name='norm2') + # pool2 + pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], + strides=[1, 2, 2, 1], padding='SAME', name='pool2') + + # local3 + with tf.variable_scope('local3') as scope: + # Move everything into depth so we can perform a single matrix multiply. + dim = 1 + for d in pool2.get_shape()[1:].as_list(): + dim *= d + reshape = tf.reshape(pool2, [FLAGS.batch_size, dim]) + + weights = _variable_with_weight_decay('weights', shape=[dim, 384], + stddev=0.04, wd=0.004) + biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1)) + local3 = tf.nn.relu_layer(reshape, weights, biases, name=scope.name) + _activation_summary(local3) + + # local4 + with tf.variable_scope('local4') as scope: + weights = _variable_with_weight_decay('weights', shape=[384, 192], + stddev=0.04, wd=0.004) + biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1)) + local4 = tf.nn.relu_layer(local3, weights, biases, name=scope.name) + _activation_summary(local4) + + # softmax, i.e. softmax(WX + b) + with tf.variable_scope('softmax_linear') as scope: + weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES], + stddev=1/192.0, wd=0.0) + biases = _variable_on_cpu('biases', [NUM_CLASSES], + tf.constant_initializer(0.0)) + softmax_linear = tf.nn.xw_plus_b(local4, weights, biases, name=scope.name) + _activation_summary(softmax_linear) + + return softmax_linear + + +def loss(logits, labels): + """Add L2Loss to all the trainable variables. + + Add summary for for "Loss" and "Loss/avg". + Args: + logits: Logits from inference(). + labels: Labels from distorted_inputs or inputs(). 1-D tensor + of shape [batch_size] + + Returns: + Loss tensor of type float. + """ + # Reshape the labels into a dense Tensor of + # shape [batch_size, NUM_CLASSES]. + sparse_labels = tf.reshape(labels, [FLAGS.batch_size, 1]) + indices = tf.reshape(tf.range(0, FLAGS.batch_size, 1), [FLAGS.batch_size, 1]) + concated = tf.concat(1, [indices, sparse_labels]) + dense_labels = tf.sparse_to_dense(concated, + [FLAGS.batch_size, NUM_CLASSES], + 1.0, 0.0) + + # Calculate the average cross entropy loss across the batch. + cross_entropy = tf.nn.softmax_cross_entropy_with_logits( + logits, dense_labels, name='cross_entropy_per_example') + cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') + tf.add_to_collection('losses', cross_entropy_mean) + + # The total loss is defined as the cross entropy loss plus all of the weight + # decay terms (L2 loss). + return tf.add_n(tf.get_collection('losses'), name='total_loss') + + +def _add_loss_summaries(total_loss): + """Add summaries for losses in CIFAR-10 model. + + Generates moving average for all losses and associated summaries for + visualizing the performance of the network. + + Args: + total_loss: Total loss from loss(). + Returns: + loss_averages_op: op for generating moving averages of losses. + """ + # Compute the moving average of all individual losses and the total loss. + loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') + losses = tf.get_collection('losses') + loss_averages_op = loss_averages.apply(losses + [total_loss]) + + # Attach a scalar summmary to all individual losses and the total loss; do the + # same for the averaged version of the losses. + for l in losses + [total_loss]: + # Name each loss as '(raw)' and name the moving average version of the loss + # as the original loss name. + tf.scalar_summary(l.op.name +' (raw)', l) + tf.scalar_summary(l.op.name, loss_averages.average(l)) + + return loss_averages_op + + +def train(total_loss, global_step): + """Train CIFAR-10 model. + + Create an optimizer and apply to all trainable variables. Add moving + average for all trainable variables. + + Args: + total_loss: Total loss from loss(). + global_step: Integer Variable counting the number of training steps + processed. + Returns: + train_op: op for training. + """ + # Variables that affect learning rate. + num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size + decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) + + # Decay the learning rate exponentially based on the number of steps. + lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, + global_step, + decay_steps, + LEARNING_RATE_DECAY_FACTOR, + staircase=True) + tf.scalar_summary('learning_rate', lr) + + # Generate moving averages of all losses and associated summaries. + loss_averages_op = _add_loss_summaries(total_loss) + + # Compute gradients. + with tf.control_dependencies([loss_averages_op]): + opt = tf.train.GradientDescentOptimizer(lr) + grads = opt.compute_gradients(total_loss) + + # Apply gradients. + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + # Add histograms for trainable variables. + for var in tf.trainable_variables(): + tf.histogram_summary(var.op.name, var) + + # Add histograms for gradients. + for grad, var in grads: + if grad: + tf.histogram_summary(var.op.name + '/gradients', grad) + + # Track the moving averages of all trainable variables. + variable_averages = tf.train.ExponentialMovingAverage( + MOVING_AVERAGE_DECAY, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + with tf.control_dependencies([apply_gradient_op, variables_averages_op]): + train_op = tf.no_op(name='train') + + return train_op + + +def maybe_download_and_extract(): + """Download and extract the tarball from Alex's website.""" + dest_directory = FLAGS.data_dir + if not os.path.exists(dest_directory): + os.makedirs(dest_directory) + filename = DATA_URL.split('/')[-1] + filepath = os.path.join(dest_directory, filename) + if not os.path.exists(filepath): + def _progress(count, block_size, total_size): + sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename, + float(count * block_size) / float(total_size) * 100.0)) + sys.stdout.flush() + filepath, _ = urllib.urlretrieve(DATA_URL, filepath, reporthook=_progress) + print + statinfo = os.stat(filepath) + print 'Succesfully downloaded', filename, statinfo.st_size, 'bytes.' + tarfile.open(filepath, 'r:gz').extractall(dest_directory) |