def main(_):
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
#######################
# Config model_deploy #
#######################
deploy_config = model_deploy.DeploymentConfig(
num_clones=FLAGS.num_clones,
clone_on_cpu=FLAGS.clone_on_cpu,
replica_id=FLAGS.task,
num_replicas=FLAGS.worker_replicas,
num_ps_tasks=FLAGS.num_ps_tasks)
# Create global_step
with tf.device(deploy_config.variables_device()):
global_step = slim.create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_biasCNN.get_dataset(
FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
######################
# Select the network #
######################
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
weight_decay=FLAGS.weight_decay,
is_training=True)
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_biasCNN.get_preprocessing(
preprocessing_name,
is_training=True, flipLR = FLAGS.flipLR, random_scale = FLAGS.random_scale)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
with tf.device(deploy_config.inputs_device()):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * FLAGS.batch_size,
common_queue_min=10 * FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
train_image_size = FLAGS.train_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, train_image_size, train_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(
labels, dataset.num_classes - FLAGS.labels_offset)
batch_queue = slim.prefetch_queue.prefetch_queue(
[images, labels], capacity=2 * deploy_config.num_clones)
####################
# Define the model #
####################
def clone_fn(batch_queue):
"""Allows data parallelism by creating multiple clones of network_fn."""
images, labels = batch_queue.dequeue()
logits, end_points = network_fn(images)
#############################
# Specify the loss function #
#############################
if 'AuxLogits' in end_points:
slim.losses.softmax_cross_entropy(
end_points['AuxLogits'], labels,
label_smoothing=FLAGS.label_smoothing, weights=0.4,
scope='aux_loss')
slim.losses.softmax_cross_entropy(
logits, labels, label_smoothing=FLAGS.label_smoothing, weights=1.0)
return end_points
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
clones = model_deploy.create_clones(deploy_config, clone_fn, [batch_queue])
first_clone_scope = deploy_config.clone_scope(0)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by network_fn.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
# Add summaries for end_points.
end_points = clones[0].outputs
for end_point in end_points:
x = end_points[end_point]
summaries.add(tf.summary.histogram('activations/' + end_point, x))
summaries.add(tf.summary.scalar('sparsity/' + end_point,
tf.nn.zero_fraction(x)))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Add summaries for variables.
for variable in slim.get_model_variables():
summaries.add(tf.summary.histogram(variable.op.name, variable))
#################################
# Configure the moving averages #
#################################
if FLAGS.moving_average_decay:
moving_average_variables = slim.get_model_variables()
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, global_step)
else:
moving_average_variables, variable_averages = None, None
if FLAGS.quantize_delay >= 0:
tf.contrib.quantize.create_training_graph(
quant_delay=FLAGS.quantize_delay)
#########################################
# Configure the optimization procedure. #
#########################################
with tf.device(deploy_config.optimizer_device()):
learning_rate = _configure_learning_rate(dataset.num_samples, global_step)
optimizer = _configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
if FLAGS.sync_replicas:
# If sync_replicas is enabled, the averaging will be done in the chief
# queue runner.
optimizer = tf.train.SyncReplicasOptimizer(
opt=optimizer,
replicas_to_aggregate=FLAGS.replicas_to_aggregate,
total_num_replicas=FLAGS.worker_replicas,
variable_averages=variable_averages,
variables_to_average=moving_average_variables)
elif FLAGS.moving_average_decay:
# Update ops executed locally by trainer.
update_ops.append(variable_averages.apply(moving_average_variables))
# Variables to train.
variables_to_train = _get_variables_to_train()
# and returns a train_tensor and summary_op
total_loss, clones_gradients = model_deploy.optimize_clones(
clones,
optimizer,
var_list=variables_to_train)
# Add total_loss to summary.
summaries.add(tf.summary.scalar('total_loss', total_loss))
# Create gradient updates.
grad_updates = optimizer.apply_gradients(clones_gradients,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
first_clone_scope))
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries), name='summary_op')
###########################
# Kicks off the training. #
###########################
slim.learning.train(
train_tensor,
logdir=FLAGS.train_dir,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
init_fn=_get_init_fn(),
summary_op=summary_op,
number_of_steps=FLAGS.max_number_of_steps,
log_every_n_steps=FLAGS.log_every_n_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
sync_optimizer=optimizer if FLAGS.sync_replicas else None)
def main(_):
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
#######################
# Config model_deploy #
#######################
deploy_config = model_deploy.DeploymentConfig(
num_clones=FLAGS.num_clones,
clone_on_cpu=FLAGS.clone_on_cpu,
replica_id=FLAGS.task,
num_replicas=FLAGS.worker_replicas,
num_ps_tasks=FLAGS.num_ps_tasks)
# Create global_step
with tf.device(deploy_config.variables_device()):
global_step = slim.create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_biasCNN.get_dataset(
FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
dataset_val = dataset_biasCNN.get_dataset(
FLAGS.dataset_name, 'validation', FLAGS.dataset_dir)
######################
# Select the network #
######################
if FLAGS.weights_initializer is None:
weights_initializer = None
# default value will be defined in argscope, it is xavier_initializer
elif FLAGS.weights_initializer=='zeros':
weights_initializer = tf.zeros_initializer()
elif FLAGS.weights_initializer=='ones':
weights_initializer = tf.ones_initializer()
elif FLAGS.weights_initializer=='trunc_normal':
weights_initializer = tf.truncated_normal_initializer()
elif FLAGS.weights_initializer=='xavier':
weights_initializer = initializers.xavier_initializer()
elif FLAGS.weights_initializer=='var_scaling':
weights_initializer = initializers.variance_scaling_initializer()
else:
raise ValueError('weight initializer not found')
if FLAGS.biases_initializer is None:
biases_initializer = None
# default value will be defined in argscope, it is zeros_initializer
elif biases_initializer=='zeros':
biases_initializer = tf.zeros_initializer()
elif FLAGS.biases_initializer=='ones':
biases_initializer = tf.ones_initializer()
elif FLAGS.biases_initializer=='trunc_normal':
biases_initializer = tf.truncated_normal_initializer()
elif FLAGS.biases_initializer=='xavier':
biases_initializer = initializers.xavier_initializer()
elif FLAGS.biases_initializer=='var_scaling':
biases_initializer = initializers.variance_scaling_initializer()
else:
raise ValueError('biases initializer not found')
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
weight_decay=FLAGS.weight_decay,
weights_initializer=weights_initializer,
biases_initializer=biases_initializer,
is_training=True)
network_fn_val = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
weights_initializer=weights_initializer,
biases_initializer=biases_initializer,
is_training=False)
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_biasCNN.get_preprocessing(
preprocessing_name,
is_training=True, flipLR = FLAGS.flipLR, random_scale = FLAGS.random_scale,
is_windowed = FLAGS.is_windowed)
image_preprocessing_fn_val = preprocessing_biasCNN.get_preprocessing(
preprocessing_name,
is_training=False, flipLR = FLAGS.flipLR, random_scale = FLAGS.random_scale,
is_windowed=FLAGS.is_windowed)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
with tf.device(deploy_config.inputs_device()):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * FLAGS.batch_size,
common_queue_min=10 * FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
train_image_size = FLAGS.train_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, train_image_size, train_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(
labels, dataset.num_classes - FLAGS.labels_offset)
batch_queue = slim.prefetch_queue.prefetch_queue(
[images, labels], capacity=2 * deploy_config.num_clones)
############################################
# Create a provider for the validation set #
############################################
provider_val = slim.dataset_data_provider.DatasetDataProvider(
dataset_val,
shuffle=True,
common_queue_capacity=2 * FLAGS.batch_size_val,
common_queue_min=FLAGS.batch_size_val)
[image_val, label_val] = provider_val.get(['image', 'label'])
label_val -= FLAGS.labels_offset
eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size
image_val = image_preprocessing_fn_val(image_val, eval_image_size, eval_image_size)
images_val, labels_val = tf.train.batch(
[image_val, label_val],
batch_size=FLAGS.batch_size_val,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size_val)
labels_val_onehot = slim.one_hot_encoding(
labels_val, dataset.num_classes - FLAGS.labels_offset)
###############################
# Define the model (training) #
###############################
def clone_fn(batch_queue):
"""Allows data parallelism by creating multiple clones of network_fn."""
images, labels = batch_queue.dequeue()
with tf.variable_scope('my_scope'):
logits, end_points = network_fn(images)
#############################
# Specify the loss function #
#############################
if 'AuxLogits' in end_points:
slim.losses.softmax_cross_entropy(
end_points['AuxLogits'], labels,
label_smoothing=FLAGS.label_smoothing, weights=0.4,
scope='aux_loss')
tf.losses.softmax_cross_entropy(
labels, logits, label_smoothing=FLAGS.label_smoothing, weights=1.0)
return end_points
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
clones = model_deploy.create_clones(deploy_config, clone_fn, [batch_queue])
first_clone_scope = deploy_config.clone_scope(0)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by network_fn.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
# Add summaries for end_points.
end_points = clones[0].outputs
for end_point in end_points:
x = end_points[end_point]
# adding in a picture of the activations at each layer, this is a good way to double check that the rotated images look rotated to our eyes
if 'conv' in end_point:
dims = x.get_shape()
for ii in range(5):
summaries.add(tf.summary.image('image_out/' + end_point + '/image_' + str(ii), tf.slice(x,[ii,0,0,0],[1,dims[1],dims[2],1])))
summaries.add(tf.summary.histogram('activations/' + end_point, x))
summaries.add(tf.summary.scalar('sparsity/' + end_point,
tf.nn.zero_fraction(x)))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Add summaries for variables.
for variable in slim.get_model_variables():
summaries.add(tf.summary.histogram(variable.op.name, variable))
#################################
# Configure the moving averages #
#################################
if FLAGS.moving_average_decay:
moving_average_variables = slim.get_model_variables()
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, global_step)
else:
moving_average_variables, variable_averages = None, None
if FLAGS.quantize_delay >= 0:
tf.contrib.quantize.create_training_graph(
quant_delay=FLAGS.quantize_delay)
#########################################
# Configure the optimization procedure. #
#########################################
with tf.device(deploy_config.optimizer_device()):
learning_rate = _configure_learning_rate(dataset.num_samples, global_step)
optimizer = _configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
if FLAGS.sync_replicas:
# If sync_replicas is enabled, the averaging will be done in the chief
# queue runner.
optimizer = tf.train.SyncReplicasOptimizer(
opt=optimizer,
replicas_to_aggregate=FLAGS.replicas_to_aggregate,
total_num_replicas=FLAGS.worker_replicas,
variable_averages=variable_averages,
variables_to_average=moving_average_variables)
elif FLAGS.moving_average_decay:
# Update ops executed locally by trainer.
update_ops.append(variable_averages.apply(moving_average_variables))
# Variables to train.
variables_to_train = _get_variables_to_train()
# and returns a train_tensor and summary_op
total_loss, clones_gradients = model_deploy.optimize_clones(
clones,
optimizer,
var_list=variables_to_train)
# Add total_loss to summary.
summaries.add(tf.summary.scalar('total_loss', total_loss))
# Create gradient updates.
grad_updates = optimizer.apply_gradients(clones_gradients,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
first_clone_scope))
#################################
# Define the model (validation) #
#################################
# get the validation set logits (predictions)
with tf.variable_scope('my_scope',reuse=True):
logits_val, _ = network_fn_val(images_val)
predictions_val = tf.argmax(logits_val, 1)
# Define loss on validation set, add a summary
tf.losses.softmax_cross_entropy(
labels_val_onehot, logits_val, label_smoothing=FLAGS.label_smoothing,
weights=1.0, loss_collection = 'eval_losses')
for loss in tf.get_collection('eval_losses'):
summaries.add(tf.summary.scalar('eval_losses/%s' % loss.op.name, loss))
# Define the validation set metrics:
# Will define each metric twice as separate operation.
# One set will be made resettable, the other set will be streaming.
with tf.name_scope('eval_metrics'):
eval_acc_value, eval_acc_op = tf.metrics.accuracy(predictions=predictions_val,labels=labels_val)
eval_recall_5_value, eval_recall_5_op = slim.metrics.streaming_recall_at_k(predictions=logits_val, labels=labels_val,k=5)
# add these variables as summaries for tensorboard
summaries.add(tf.summary.scalar('eval_recall_5', eval_recall_5_value))
summaries.add(tf.summary.scalar('eval_acc', eval_acc_value))
with tf.name_scope('eval_metrics_streaming'):
eval_acc_streaming_value, eval_acc_streaming_op = tf.metrics.accuracy(predictions=predictions_val,labels=labels_val)
eval_recall_5_streaming_value, eval_recall_5_streaming_op = slim.metrics.streaming_recall_at_k(predictions=logits_val, labels=labels_val,k=5)
# add these variables as summaries for tensorboard
summaries.add(tf.summary.scalar('eval_recall_5_streaming', eval_recall_5_streaming_value))
summaries.add(tf.summary.scalar('eval_acc_streaming', eval_acc_streaming_value))
# also add summaries of all the local variables used to compute the eval metrics...
for metric in tf.get_collection(tf.GraphKeys.METRIC_VARIABLES, 'eval_metrics'):
summaries.add(tf.summary.scalar('%s' % metric.op.name, metric))
for metric in tf.get_collection(tf.GraphKeys.METRIC_VARIABLES, 'eval_streaming_metrics'):
summaries.add(tf.summary.scalar('%s' % metric.op.name, metric))
# gather up all the variables that are used to compute eval metrics
stream_vars = [i for i in tf.local_variables() if i.name.split('/')[0]=='eval_metrics']
# make an operation that'll let us re-initialize just these vars.
reset_op = tf.initialize_variables(stream_vars)
# make an operation that'll let us run evaluation (all metrics)
eval_op = list([eval_acc_op, eval_recall_5_op, eval_acc_streaming_op, eval_recall_5_streaming_op])
# Gather validation summaries
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES))
# Merge all summaries together (this includes training summaries too).
summary_op = tf.summary.merge(list(summaries), name='summary_op')
# Create a non-default saver so we don't delete all the old checkpoints.
my_saver = tf_saver.Saver(max_to_keep=FLAGS.max_checkpoints_to_keep,
keep_checkpoint_every_n_hours=FLAGS.keep_checkpoint_every_n_hours,)
# Create a non-default dictionary of options for train_step_fn
# This is a hack that lets us pass everything we need to run evaluation, into the training loop function
with ops.name_scope('train_step'):
train_step_kwargs = {}
if FLAGS.max_number_of_steps:
should_stop_op = math_ops.greater_equal(global_step, FLAGS.max_number_of_steps)
else:
should_stop_op = constant_op.constant(False)
train_step_kwargs['should_stop'] = should_stop_op
if FLAGS.log_every_n_steps > 0:
train_step_kwargs['should_log'] = math_ops.equal(
math_ops.mod(global_step, FLAGS.log_every_n_steps), 0)
train_step_kwargs['should_val'] = math_ops.equal(
math_ops.mod(global_step, FLAGS.val_every_n_steps),0)
train_step_kwargs['should_reset_eval_metrics'] = math_ops.equal(
math_ops.mod(global_step, tf.to_int64(math_ops.multiply(FLAGS.reset_eval_metrics_every_n_vals, FLAGS.val_every_n_steps))),0)
train_step_kwargs['eval_op'] = eval_op
train_step_kwargs['reset_op'] = reset_op
###########################
# Kicks off the training. #
###########################
slim.learning.train(
train_tensor,
logdir=FLAGS.train_dir,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
init_fn=_get_init_fn(),
summary_op=summary_op,
number_of_steps=FLAGS.max_number_of_steps,
log_every_n_steps=FLAGS.log_every_n_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
sync_optimizer=optimizer if FLAGS.sync_replicas else None,
saver=my_saver,
train_step_fn=learning_biasCNN.train_step_fn,
train_step_kwargs = train_step_kwargs)
def main(_):
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
num_batches = FLAGS.num_batches
# for bb in np.arange(0,num_batches):
for bb in [0]:
batch_name = 'batch' + str(bb)
# tf.app.flags.DEFINE_string(
# 'dataset_split_name',batch_name, 'The name of the train/test split.')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
tf_global_step = slim.get_or_create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_biasCNN.get_dataset(
FLAGS.dataset_name,
batch_name,
FLAGS.dataset_dir,
num_classes=FLAGS.num_classes)
####################
# Select the model #
####################
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
is_training=False)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=1,
shuffle=False,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_biasCNN.get_preprocessing(
preprocessing_name,
is_training=False,
flipLR=False,
random_scale=False,
is_windowed=FLAGS.is_windowed)
eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, eval_image_size,
eval_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
# ims_orig = tf.identity(images);
# labels_orig = tf.identity(labels);
####################
# Define the model #
####################
logits, end_pts = network_fn(images)
if FLAGS.moving_average_decay:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, tf_global_step)
variables_to_restore = variable_averages.variables_to_restore(
slim.get_model_variables())
variables_to_restore[tf_global_step.op.name] = tf_global_step
else:
if FLAGS.append_scope_string:
# If I've specified a string for the name of the scope in the checkpoint file, append it here so we can match up the layer names
variables_to_restore_orig = slim.get_variables_to_restore()
variables_to_restore = {}
for var in variables_to_restore_orig:
curr_name = var.op.name
if 'global_step' not in curr_name:
new_name = FLAGS.append_scope_string + '/' + curr_name
else:
new_name = curr_name
variables_to_restore[new_name] = var
else:
variables_to_restore = slim.get_variables_to_restore()
predictions = tf.argmax(logits, 1)
labels = tf.squeeze(labels)
# Define the metrics:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(
{
'Accuracy':
slim.metrics.streaming_accuracy(predictions, labels),
'Recall_5':
slim.metrics.streaming_recall_at_k(logits, labels, 5),
})
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
# Add summaries for first layer to visualize the image, double check whether rotations are CW or CCW
keylist = list(end_pts.keys())
x = end_pts[keylist[0]]
dims = x.get_shape()
for ii in range(dims[0]):
summaries.add(
tf.summary.image(
'image_out/' + keylist[0] + '/label_' + str(ii),
tf.slice(x, [ii, 0, 0, 0], [1, dims[1], dims[2], 1])))
summaries.add(
tf.summary.scalar('image_label/label_' + str(ii),
labels[ii]))
# Print the summaries to screen.
for name, value in names_to_values.items():
summary_name = 'eval/%s' % name
op = tf.summary.scalar(summary_name, value, collections=[])
op = tf.Print(op, [value], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# Merge all summaries together (this includes training summaries too).
summary_op = tf.summary.merge(list(summaries), name='summary_op')
# TODO(sguada) use num_epochs=1
if FLAGS.max_num_batches:
num_batches = FLAGS.max_num_batches
else:
# This ensures that we make a single pass over all of the data.
num_batches = math.ceil(dataset.num_samples /
float(FLAGS.batch_size))
if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(
FLAGS.checkpoint_path)
else:
checkpoint_path = FLAGS.checkpoint_path
tf.logging.info('Evaluating %s' % checkpoint_path)
out = slim.evaluation.evaluate_once(
master=FLAGS.master,
checkpoint_path=checkpoint_path,
logdir=FLAGS.eval_dir,
num_evals=num_batches,
eval_op=list(names_to_updates.values()),
summary_op=summary_op,
final_op={
'logits': logits,
'end_pts': end_pts,
'images': images,
'labels': labels,
'predictions': predictions
},
variables_to_restore=variables_to_restore)
def main(_):
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
#######################
# Config model_deploy #
#######################
deploy_config = model_deploy.DeploymentConfig(
num_clones=FLAGS.num_clones,
clone_on_cpu=FLAGS.clone_on_cpu,
replica_id=FLAGS.task,
num_replicas=FLAGS.worker_replicas,
num_ps_tasks=FLAGS.num_ps_tasks)
# Create global_step
with tf.device(deploy_config.variables_device()):
global_step = slim.create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_biasCNN.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.dataset_dir)
dataset_val = dataset_biasCNN.get_dataset(FLAGS.dataset_name,
'validation',
FLAGS.dataset_dir)
######################
# Select the network #
######################
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
weight_decay=FLAGS.weight_decay,
is_training=True)
network_fn_val = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
is_training=False)
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_biasCNN.get_preprocessing(
preprocessing_name,
is_training=True,
flipLR=FLAGS.flipLR,
random_scale=FLAGS.random_scale,
is_windowed=FLAGS.is_windowed)
image_preprocessing_fn_val = preprocessing_biasCNN.get_preprocessing(
preprocessing_name,
is_training=False,
flipLR=FLAGS.flipLR,
random_scale=FLAGS.random_scale,
is_windowed=FLAGS.is_windowed)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
with tf.device(deploy_config.inputs_device()):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * FLAGS.batch_size,
common_queue_min=10 * FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
train_image_size = FLAGS.train_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, train_image_size,
train_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(
labels, dataset.num_classes - FLAGS.labels_offset)
batch_queue = slim.prefetch_queue.prefetch_queue(
[images, labels], capacity=2 * deploy_config.num_clones)
############################################
# Create a provider for the validation set #
############################################
provider_val = slim.dataset_data_provider.DatasetDataProvider(
dataset_val,
shuffle=True,
common_queue_capacity=2 * FLAGS.batch_size_val,
common_queue_min=FLAGS.batch_size_val)
[image_val, label_val] = provider_val.get(['image', 'label'])
label_val -= FLAGS.labels_offset
eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size
image_val = image_preprocessing_fn_val(image_val, eval_image_size,
eval_image_size)
images_val, labels_val = tf.train.batch(
[image_val, label_val],
batch_size=FLAGS.batch_size_val,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size_val)
###############################
# Define the model (training) #
###############################
def clone_fn(batch_queue):
"""Allows data parallelism by creating multiple clones of network_fn."""
images, labels = batch_queue.dequeue()
with tf.variable_scope('my_scope'):
logits, end_points = network_fn(images)
#############################
# Specify the loss function #
#############################
if 'AuxLogits' in end_points:
slim.losses.softmax_cross_entropy(
end_points['AuxLogits'],
labels,
label_smoothing=FLAGS.label_smoothing,
weights=0.4,
scope='aux_loss')
slim.losses.softmax_cross_entropy(
logits,
labels,
label_smoothing=FLAGS.label_smoothing,
weights=1.0)
return end_points
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
clones = model_deploy.create_clones(deploy_config, clone_fn,
[batch_queue])
first_clone_scope = deploy_config.clone_scope(0)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by network_fn.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
first_clone_scope)
# Add summaries for end_points.
end_points = clones[0].outputs
for end_point in end_points:
x = end_points[end_point]
summaries.add(tf.summary.histogram('activations/' + end_point, x))
summaries.add(
tf.summary.scalar('sparsity/' + end_point,
tf.nn.zero_fraction(x)))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Add summaries for variables.
for variable in slim.get_model_variables():
summaries.add(tf.summary.histogram(variable.op.name, variable))
#################################
# Configure the moving averages #
#################################
if FLAGS.moving_average_decay:
moving_average_variables = slim.get_model_variables()
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, global_step)
else:
moving_average_variables, variable_averages = None, None
if FLAGS.quantize_delay >= 0:
tf.contrib.quantize.create_training_graph(
quant_delay=FLAGS.quantize_delay)
#########################################
# Configure the optimization procedure. #
#########################################
with tf.device(deploy_config.optimizer_device()):
learning_rate = _configure_learning_rate(dataset.num_samples,
global_step)
optimizer = _configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
if FLAGS.sync_replicas:
# If sync_replicas is enabled, the averaging will be done in the chief
# queue runner.
optimizer = tf.train.SyncReplicasOptimizer(
opt=optimizer,
replicas_to_aggregate=FLAGS.replicas_to_aggregate,
total_num_replicas=FLAGS.worker_replicas,
variable_averages=variable_averages,
variables_to_average=moving_average_variables)
elif FLAGS.moving_average_decay:
# Update ops executed locally by trainer.
update_ops.append(
variable_averages.apply(moving_average_variables))
# Variables to train.
variables_to_train = _get_variables_to_train()
# and returns a train_tensor and summary_op
total_loss, clones_gradients = model_deploy.optimize_clones(
clones, optimizer, var_list=variables_to_train)
# Add total_loss to summary.
summaries.add(tf.summary.scalar('total_loss', total_loss))
# Create gradient updates.
grad_updates = optimizer.apply_gradients(clones_gradients,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(
tf.get_collection(tf.GraphKeys.SUMMARIES, first_clone_scope))
#################################
# Define the model (validation) #
#################################
with tf.variable_scope('my_scope', reuse=True):
logits_val, _ = network_fn_val(images_val)
predictions_val = tf.argmax(logits_val, 1)
labels_val = tf.squeeze(labels_val)
# Define the metrics:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
'Accuracy':
slim.metrics.streaming_accuracy(predictions_val, labels_val),
'Recall_5':
slim.metrics.streaming_recall_at_k(logits_val, labels_val, 5)
})
for name, value in names_to_values.items():
summary_name = 'eval/%s' % name
op = tf.summary.scalar(summary_name, value, collections=[])
op = tf.Print(op, [value], summary_name)
tf.add_to_collection('summaries', op)
# Gather validation summaries
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES))
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries), name='summary_op')
# Create a non-default saver so we don't delete all the old checkpoints.
my_saver = tf_saver.Saver(
max_to_keep=FLAGS.max_checkpoints_to_keep,
keep_checkpoint_every_n_hours=FLAGS.keep_checkpoint_every_n_hours,
)
# Create a non-default dictionary of options for train_step_fn
# This is a hack that lets us pass everything we need to run evaluation, into the training loop function
from tensorflow.python.framework import ops
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import math_ops
with ops.name_scope('train_step'):
train_step_kwargs = {}
if FLAGS.max_number_of_steps:
should_stop_op = math_ops.greater_equal(
global_step, FLAGS.max_number_of_steps)
else:
should_stop_op = constant_op.constant(False)
train_step_kwargs['should_stop'] = should_stop_op
if FLAGS.log_every_n_steps > 0:
train_step_kwargs['should_log'] = math_ops.equal(
math_ops.mod(global_step, FLAGS.log_every_n_steps), 0)
train_step_kwargs['should_val'] = math_ops.equal(
math_ops.mod(global_step, FLAGS.val_every_n_steps), 0)
train_step_kwargs['eval_op'] = list(names_to_updates.values())
# assert(FLAGS.max_number_of_steps==100000)
print(should_stop_op)
###########################
# Kicks off the training. #
###########################
slim.learning.train(
train_tensor,
logdir=FLAGS.train_dir,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
init_fn=_get_init_fn(),
summary_op=summary_op,
number_of_steps=FLAGS.max_number_of_steps,
log_every_n_steps=FLAGS.log_every_n_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
sync_optimizer=optimizer if FLAGS.sync_replicas else None,
saver=my_saver,
train_step_fn=learning_biasCNN.train_step_fn,
train_step_kwargs=train_step_kwargs)
def main(_):
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
num_batches= FLAGS.num_batches
for bb in np.arange(0,num_batches):
batch_name = 'batch'+str(bb)
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
tf_global_step = slim.get_or_create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_biasCNN.get_dataset(
FLAGS.dataset_name, batch_name, FLAGS.dataset_dir, num_classes=FLAGS.num_classes)
####################
# Select the model #
####################
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
is_training=False)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=1,
shuffle=False,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_biasCNN.get_preprocessing(
preprocessing_name,
is_training=False, flipLR=False,random_scale=False,
is_windowed=FLAGS.is_windowed)
eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
####################
# Define the model #
####################
logits, end_pts = network_fn(images)
if FLAGS.moving_average_decay:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, tf_global_step)
variables_to_restore = variable_averages.variables_to_restore(
slim.get_model_variables())
variables_to_restore[tf_global_step.op.name] = tf_global_step
else:
if FLAGS.append_scope_string:
# If I've specified a string for the name of the scope in the checkpoint file, append it here so we can match up the layer names
variables_to_restore_orig = slim.get_variables_to_restore()
variables_to_restore = {}
for var in variables_to_restore_orig:
curr_name = var.op.name
if 'global_step' not in curr_name:
new_name = FLAGS.append_scope_string + '/' + curr_name
else:
new_name = curr_name
variables_to_restore[new_name]= var
else:
variables_to_restore = slim.get_variables_to_restore()
predictions = tf.argmax(logits, 1)
labels = tf.squeeze(labels)
# Define the metrics:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
'Accuracy': slim.metrics.streaming_accuracy(predictions, labels),
'Recall_5': slim.metrics.streaming_recall_at_k(
logits, labels, 5),
})
# Print the summaries to screen.
for name, value in names_to_values.items():
summary_name = 'eval/%s' % name
op = tf.summary.scalar(summary_name, value, collections=[])
op = tf.Print(op, [value], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# TODO(sguada) use num_epochs=1
if FLAGS.max_num_batches:
num_batches = FLAGS.max_num_batches
else:
# This ensures that we make a single pass over all of the data.
num_batches = math.ceil(dataset.num_samples / float(FLAGS.batch_size))
if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
else:
checkpoint_path = FLAGS.checkpoint_path
tf.logging.info('Evaluating %s' % checkpoint_path)
out = slim.evaluation.evaluate_once(
master=FLAGS.master,
checkpoint_path=checkpoint_path,
logdir=FLAGS.eval_dir,
num_evals=num_batches,
eval_op=list(names_to_updates.values()),
final_op={'logits':logits, 'end_pts':end_pts,'images':images,'labels':labels,'predictions':predictions},
variables_to_restore=variables_to_restore)
end_pts= out['end_pts']
keylist= list(end_pts.keys())
for kk in range(np.size(keylist)):
keystr = keylist[kk]
keystr = keystr.replace('/','_')
fn2save = FLAGS.eval_dir + '/' + batch_name + '_' + keystr + '.npy'
np.save(fn2save, end_pts[keylist[kk]])
logits = out['logits']
labels = out['labels']
predictions = out['predictions']
fn2save = FLAGS.eval_dir + '/' + batch_name + '_logits.npy'
np.save(fn2save, logits)
fn2save = FLAGS.eval_dir + '/' + batch_name + '_labels_orig.npy'
np.save(fn2save, labels)
fn2save = FLAGS.eval_dir + '/' + batch_name + '_labels_predicted.npy'
np.save(fn2save, predictions)
