def initialize_graph(graph):
with graph.as_default():
dataset = dataset_factory.get_dataset(
"characters",
"validation",
os.environ.get("DATASET_DIR")
)
image_processing_fn = preprocessing_factory.get_preprocessing(
"inception_v4",
is_training=False
)
checkpoint_path = tf.train.latest_checkpoint(os.environ.get("CHECKPOINTS_DIR"))
network_fn = nets_factory.get_network_fn(
"inception_v4",
num_classes=dataset.num_classes,
is_training=False,
reuse=None
)
eval_image_size = network_fn.default_image_size
placeholder = tf.expand_dims(tf.placeholder(tf.float32, shape=(eval_image_size, eval_image_size, 3)), 0)
network_fn(placeholder)
variables_to_restore = tf.contrib.slim.get_variables_to_restore()
init_fn = tf.contrib.slim.assign_from_checkpoint_fn(checkpoint_path, variables_to_restore)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.25)
tf_sess_config = tf.ConfigProto(gpu_options=gpu_options)
session = tf.Session(graph=graph, config=tf_sess_config)
init_fn(session)
return (dataset, image_processing_fn, session)
def main(_):
with tf.Graph().as_default():
dataset = dataset_factory.get_dataset(
FLAGS.dataset_name,
"validation",
FLAGS.dataset_dir
)
image_processing_fn = preprocessing_factory.get_preprocessing(
"inception_v4",
is_training=False,
fast_mode=True
)
with open(os.path.join(FLAGS.dataset_dir, "labels.txt"), "r") as f:
labels = [re.sub(r"^\d+:", "", x) for x in f.read().split()]
reuse = False
for i in range(1, 25):
path = get_random_image()
md5 = get_md5(path)
label_path = get_label_path(md5)
if not os.path.isfile(label_path):
continue
print(path)
results = classify_image(path, labels, dataset, image_processing_fn, reuse)
reuse = True
with open(label_path, "r") as f:
actual_label = " ".join(f.read().split())
for score, label in results:
print(label, "%.2f" % score)
print("actual: {}".format(actual_label))
print("\n\n\n")
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_factory.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_factory.get_preprocessing(
preprocessing_name,
is_training=True,
fast_mode=True)
##############################################################
# 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)
if not FLAGS.multilabel:
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 FLAGS.multilabel:
if 'AuxLogits' in end_points:
tf.losses.sigmoid_cross_entropy(
labels, end_points['AuxLogits'],
label_smoothing=FLAGS.label_smoothing, weights=0.4, scope='aux_loss')
tf.losses.sigmoid_cross_entropy(
labels, logits,
label_smoothing=FLAGS.label_smoothing, weights=1.0)
else:
if 'AuxLogits' in end_points:
tf.losses.softmax_cross_entropy(
logits=end_points['AuxLogits'], onehot_labels=labels,
label_smoothing=FLAGS.label_smoothing, weights=0.4, scope='aux_loss')
tf.losses.softmax_cross_entropy(
logits=logits, onehot_labels=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
#########################################
# 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,
variable_averages=variable_averages,
variables_to_average=moving_average_variables,
replica_id=tf.constant(FLAGS.task, tf.int32, shape=()),
total_num_replicas=FLAGS.worker_replicas)
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)
from dotenv import load_dotenv
import json
import re
import numpy as np
import tensorflow as tf
from slimception.datasets import characters
from slimception.preprocessing import preprocessing_factory
from slimception.nets import nets_factory
from slimception.datasets import dataset_factory
load_dotenv("/etc/ccs/env")
graph = tf.Graph()
with graph.as_default():
dataset = dataset_factory.get_dataset("characters", "validation",
os.environ.get("DATASET_DIR"))
image_processing_fn = preprocessing_factory.get_preprocessing(
"inception_v4", is_training=False)
checkpoint_path = tf.train.latest_checkpoint(
os.environ.get("CHECKPOINTS_DIR"))
network_fn = nets_factory.get_network_fn("inception_v4",
num_classes=dataset.num_classes,
is_training=False,
reuse=None)
eval_image_size = network_fn.default_image_size
placeholder = tf.expand_dims(
tf.placeholder(tf.float32,
shape=(eval_image_size, eval_image_size, 3)), 0)
network_fn(placeholder)
variables_to_restore = tf.contrib.slim.get_variables_to_restore()
init_fn = tf.contrib.slim.assign_from_checkpoint_fn(
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():
tf_global_step = slim.get_or_create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_factory.get_dataset(FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
####################
# 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,
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_factory.get_preprocessing(
preprocessing_name,
is_training=False,
fast_mode=True)
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, _ = 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:
variables_to_restore = slim.get_variables_to_restore()
if FLAGS.multilabel:
probabilities = tf.nn.elu(logits)
probabilities = tf.Print(probabilities, [probabilities], message="probabilities: ", summarize=dataset.num_classes)
predictions = tf.round(probabilities)
predictions = tf.Print(predictions, [predictions], message="predictions: ", summarize=dataset.num_classes)
labels = tf.cast(labels, tf.int64)
labels = tf.Print(labels, [labels], message="labels: ", summarize=dataset.num_classes)
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
'precision': slim.metrics.streaming_precision(predictions, labels),
'recall': slim.metrics.streaming_recall(predictions, labels)
})
else:
predictions = tf.argmax(logits, 1)
labels = tf.squeeze(labels)
predictions = tf.Print(predictions, [predictions], message="predictions: ", summarize=dataset.num_classes)
labels = tf.Print(labels, [labels], message="labels: ", summarize=dataset.num_classes)
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
'Precision': slim.metrics.streaming_precision(predictions, labels),
'Recall': slim.metrics.streaming_recall(predictions, labels, 5)
})
# Define the metrics:
# 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)
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()),
variables_to_restore=variables_to_restore)