def inference(images,
num_classes,
for_training=False,
restore_logits=True,
scope=None):
"""Build Inception v3 model architecture.
See here for reference: http://arxiv.org/abs/1512.00567
Args:
images: Images returned from inputs() or distorted_inputs().
num_classes: number of classes
for_training: If set to `True`, build the inference model for training.
Kernels that operate differently for inference during training
e.g. dropout, are appropriately configured.
restore_logits: whether or not the logits layers should be restored.
Useful for fine-tuning a model with different num_classes.
scope: optional prefix string identifying the ImageNet tower.
Returns:
Logits. 2-D float Tensor.
Auxiliary Logits. 2-D float Tensor of side-head. Used for training only.
"""
# Parameters for BatchNorm.
batch_norm_params = {
# Decay for the moving averages.
'decay': BATCHNORM_MOVING_AVERAGE_DECAY,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
}
# Set weight_decay for weights in Conv and FC layers.
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
with slim.arg_scope([slim.ops.conv2d],
stddev=0.1,
activation=tf.nn.relu,
batch_norm_params=batch_norm_params):
logits, endpoints = slim.inception.inception_v3(
images,
dropout_keep_prob=0.8,
num_classes=num_classes,
is_training=for_training,
restore_logits=restore_logits,
scope=scope)
# Add summaries for viewing model statistics on TensorBoard.
_activation_summaries(endpoints)
# Grab the logits associated with the side head. Employed during training.
auxiliary_logits = endpoints['aux_logits']
return logits, auxiliary_logits
def inference(images,
num_classes,
for_training=False,
restore_logits=True,
scope=None,
reuse=False,
dropout_keep_prob=0.8,
verbose=False):
batch_norm_params = {
# Decay for the moving averages.
'decay': FLAGS.batchnorm_moving_average_decay,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
}
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
with slim.arg_scope([slim.ops.conv2d],
stddev=0.1,
activation=tf.nn.relu,
batch_norm_params=batch_norm_params):
# endpoints = {"logits", "predictions"}, predictions is softmax of logits
logits, endpoints = slim.inception.inception_v3(
images,
dropout_keep_prob=dropout_keep_prob,
num_classes=num_classes,
is_training=for_training,
restore_logits=restore_logits,
scope=scope,
reuse=reuse)
# Add summaries for viewing model statistics on TensorBoard.
# _activation_summaries(endpoints)
# Grab the logits associated with the side head. Employed during training.
auxiliary_logits = endpoints['aux_logits']
softmax = endpoints['predictions']
if verbose:
print logits.get_shape()
print softmax.get_shape()
# logits: output of final layer, auxliary_logits: output of hidden layer, softmax: predictions
return logits, auxiliary_logits, softmax
def vgg16(inputs, n_out=100): from slim import slim with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], stddev=0.01, weight_decay=0.0005): net = slim.ops.repeat_op(2, inputs, slim.ops.conv2d, 64, [3, 3], scope='conv1') net = slim.ops.max_pool(net, [2, 2], scope='pool1') net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 128, [3, 3], scope='conv2') net = slim.ops.max_pool(net, [2, 2], scope='pool2') net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 256, [3, 3], scope='conv3') net = slim.ops.max_pool(net, [2, 2], scope='pool3') net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv4') net = slim.ops.max_pool(net, [2, 2], scope='pool4') net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv5') # Add a 1x1 conv as a classifier net = slim.ops.conv2d(net, n_out, [1,1], scope='fc', activation=None) return net
def inference(images, num_classes, for_training=False, restore_logits=True, scope=None, reuse=False, dropout_keep_prob=0.8):
batch_norm_params = {
# Decay for the moving averages.
'decay': FLAGS.batchnorm_moving_average_decay,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
}
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
with slim.arg_scope([slim.ops.conv2d],
stddev=0.1,
activation=tf.nn.relu,
batch_norm_params=batch_norm_params):
# endpoints = {"logits", "predictions"}, predictions is softmax of logits
logits, endpoints = slim.inception.inception_v3(
images,
dropout_keep_prob=dropout_keep_prob,
num_classes=num_classes,
is_training=for_training,
restore_logits=restore_logits,
scope=scope,
reuse=reuse)
# Add summaries for viewing model statistics on TensorBoard.
# _activation_summaries(endpoints)
# Grab the logits associated with the side head. Employed during training.
auxiliary_logits = endpoints['aux_logits']
softmax = endpoints['predictions']
print logits.get_shape()
print softmax.get_shape()
# logits: output of final layer, auxliary_logits: output of hidden layer, softmax: predictions
return logits, auxiliary_logits, softmax
def vgg16(inputs, n_out=100):
from slim import slim
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc],
stddev=0.01,
weight_decay=0.0005):
net = slim.ops.repeat_op(2,
inputs,
slim.ops.conv2d,
64, [3, 3],
scope='conv1')
net = slim.ops.max_pool(net, [2, 2], scope='pool1')
net = slim.ops.repeat_op(2,
net,
slim.ops.conv2d,
128, [3, 3],
scope='conv2')
net = slim.ops.max_pool(net, [2, 2], scope='pool2')
net = slim.ops.repeat_op(3,
net,
slim.ops.conv2d,
256, [3, 3],
scope='conv3')
net = slim.ops.max_pool(net, [2, 2], scope='pool3')
net = slim.ops.repeat_op(3,
net,
slim.ops.conv2d,
512, [3, 3],
scope='conv4')
net = slim.ops.max_pool(net, [2, 2], scope='pool4')
net = slim.ops.repeat_op(3,
net,
slim.ops.conv2d,
512, [3, 3],
scope='conv5')
# Add a 1x1 conv as a classifier
net = slim.ops.conv2d(net, n_out, [1, 1], scope='fc', activation=None)
return net
def train(dataset):
"""Train on dataset for a number of steps."""
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
# Calculate the learning rate schedule.
num_batches_per_epoch = (dataset.num_examples_per_epoch() /
FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch * FLAGS.num_epochs_per_decay)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(FLAGS.initial_learning_rate,
global_step,
decay_steps,
FLAGS.learning_rate_decay_factor,
staircase=True)
# Create an optimizer that performs gradient descent.
opt = tf.train.RMSPropOptimizer(lr,
RMSPROP_DECAY,
momentum=RMSPROP_MOMENTUM,
epsilon=RMSPROP_EPSILON)
# Get images and labels for ImageNet and split the batch across GPUs.
assert FLAGS.batch_size % FLAGS.num_gpus == 0, (
'Batch size must be divisible by number of GPUs')
split_batch_size = int(FLAGS.batch_size / FLAGS.num_gpus)
# Override the number of preprocessing threads to account for the increased
# number of GPU towers.
num_preprocess_threads = FLAGS.num_preprocess_threads * FLAGS.num_gpus
images, labels = image_processing.distorted_inputs(
dataset, num_preprocess_threads=num_preprocess_threads)
input_summaries = copy.copy(tf.get_collection(tf.GraphKeys.SUMMARIES))
# Number of classes in the Dataset label set plus 1.
# Label 0 is reserved for an (unused) background class.
num_classes = dataset.num_classes() + 1
# Split the batch of images and labels for towers.
images_splits = tf.split(axis=0,
num_or_size_splits=FLAGS.num_gpus,
value=images)
labels_splits = tf.split(axis=0,
num_or_size_splits=FLAGS.num_gpus,
value=labels)
# Calculate the gradients for each model tower.
tower_grads = []
reuse_variables = None
for i in range(FLAGS.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' %
(inception.TOWER_NAME, i)) as scope:
# Force all Variables to reside on the CPU.
with slim.arg_scope([slim.variables.variable],
device='/cpu:0'):
# Calculate the loss for one tower of the ImageNet model. This
# function constructs the entire ImageNet model but shares the
# variables across all towers.
loss = _tower_loss(images_splits[i], labels_splits[i],
num_classes, scope, reuse_variables)
# Reuse variables for the next tower.
reuse_variables = True
# Retain the summaries from the final tower.
summaries = tf.get_collection(tf.GraphKeys.SUMMARIES,
scope)
# Retain the Batch Normalization updates operations only from the
# final tower. Ideally, we should grab the updates from all towers
# but these stats accumulate extremely fast so we can ignore the
# other stats from the other towers without significant detriment.
batchnorm_updates = tf.get_collection(
slim.ops.UPDATE_OPS_COLLECTION, scope)
# Calculate the gradients for the batch of data on this ImageNet
# tower.
grads = opt.compute_gradients(loss)
# Keep track of the gradients across all towers.
tower_grads.append(grads)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers.
grads = _average_gradients(tower_grads)
# Add a summaries for the input processing and global_step.
summaries.extend(input_summaries)
# Add a summary to track the learning rate.
summaries.append(tf.summary.scalar('learning_rate', lr))
# Add histograms for gradients.
for grad, var in grads:
if grad is not None:
summaries.append(
tf.summary.histogram(var.op.name + '/gradients', grad))
# Apply the gradients to adjust the shared variables.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
summaries.append(tf.summary.histogram(var.op.name, var))
# Track the moving averages of all trainable variables.
# Note that we maintain a "double-average" of the BatchNormalization
# global statistics. This is more complicated then need be but we employ
# this for backward-compatibility with our previous models.
variable_averages = tf.train.ExponentialMovingAverage(
inception.MOVING_AVERAGE_DECAY, global_step)
# Another possibility is to use tf.slim.get_variables().
variables_to_average = (tf.trainable_variables() +
tf.moving_average_variables())
variables_averages_op = variable_averages.apply(variables_to_average)
# Group all updates to into a single train op.
batchnorm_updates_op = tf.group(*batchnorm_updates)
train_op = tf.group(apply_gradient_op, variables_averages_op,
batchnorm_updates_op)
# Create a saver.
saver = tf.train.Saver(tf.global_variables())
# Build the summary operation from the last tower summaries.
summary_op = tf.summary.merge(summaries)
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
if FLAGS.pretrained_model_checkpoint_path:
assert tf.gfile.Exists(FLAGS.pretrained_model_checkpoint_path)
variables_to_restore = tf.get_collection(
slim.variables.VARIABLES_TO_RESTORE)
restorer = tf.train.Saver(variables_to_restore)
restorer.restore(sess, FLAGS.pretrained_model_checkpoint_path)
print('%s: Pre-trained model restored from %s' %
(datetime.now(), FLAGS.pretrained_model_checkpoint_path))
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
graph=sess.graph)
for step in range(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
examples_per_sec = FLAGS.batch_size / float(duration)
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, duration))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 5000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)