def main(unused_argv=None):
dataset = ImagenetData(subset=FLAGS.subset)
assert dataset.data_files()
if tf.gfile.Exists(FLAGS.eval_dir):
tf.gfile.DeleteRecursively(FLAGS.eval_dir)
tf.gfile.MakeDirs(FLAGS.eval_dir)
FLAGS.dataset_name = 'imagenet'
FLAGS.num_examples = dataset.num_examples_per_epoch()
inception_eval.evaluate(dataset)
def main(_):
# Load dataset
tf.app.flags.FLAGS.data_dir = '/work/haeusser/data/imagenet/shards'
dataset = ImagenetData(subset='validation')
assert dataset.data_files()
num_labels = dataset.num_classes() + 1
image_shape = [FLAGS.image_size, FLAGS.image_size, 3]
graph = tf.Graph()
with graph.as_default():
images, labels = image_processing.batch_inputs(
dataset,
32,
train=True,
num_preprocess_threads=16,
num_readers=FLAGS.num_readers)
# Set up semisup model.
model = semisup.SemisupModel(semisup.architectures.inception_model,
num_labels,
image_shape,
test_in=images)
# Add moving average variables.
for var in tf.get_collection('moving_vars'):
tf.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, var)
for var in slim.get_model_variables():
tf.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, var)
# Get prediction tensor from semisup model.
predictions = tf.argmax(model.test_logit, 1)
# Accuracy metric for summaries.
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
'Accuracy':
slim.metrics.streaming_accuracy(predictions, labels),
})
for name, value in names_to_values.iteritems():
tf.summary.scalar(name, value)
# Run the actual evaluation loop.
num_batches = math.ceil(dataset.num_examples_per_epoch() /
float(FLAGS.eval_batch_size))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=FLAGS.logdir,
logdir=FLAGS.logdir,
num_evals=num_batches,
eval_op=names_to_updates.values(),
eval_interval_secs=FLAGS.eval_interval_secs,
session_config=config)