def evaluate():
print("in model evaluation")
dataset = dataset_module.MyDataset(subset=FLAGS.subset)
assert dataset.data_files()
FLAGS.num_examples = dataset.num_examples_per_epoch(
) / FLAGS.subsample_factor
"""Evaluate model on Dataset for a number of steps."""
with tf.Graph().as_default():
# Get images and labels from the dataset.
tensors_in, tensors_out = batching.inputs(dataset)
# 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
# Build a Graph that computes the logits predictions from the
# inference model.
logits_all = model.inference(tensors_in,
num_classes,
for_training=False)
model.loss(logits_all, tensors_out, batch_size=FLAGS.batch_size)
loss_op = slim.losses.get_losses()
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
model.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
graph_def = tf.get_default_graph().as_graph_def(add_shapes=True)
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
graph_def=graph_def)
while True:
_eval_once(saver, summary_writer, logits_all, tensors_out, loss_op,
summary_op, tensors_in)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def evaluate():
dataset = dataset_module.MyDataset(subset=FLAGS.subset)
assert dataset.data_files()
FLAGS.num_examples = dataset.num_examples_per_epoch(
) / FLAGS.subsample_factor
output_dir = os.path.dirname(FLAGS.stat_output_path)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
"""Evaluate model on Dataset for a number of steps."""
with tf.Graph().as_default():
# Get images and labels from the dataset.
tensors_in, tensors_out = batching.inputs(dataset)
config = tf.ConfigProto(intra_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Start the queue runners.
coord = tf.train.Coordinator()
try:
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(
qr.create_threads(sess,
coord=coord,
daemon=True,
start=True))
eval_method = globals()[FLAGS.eval_method]
eval_method(tensors_out, sess, coord, tensors_in)
except Exception as e: # pylint: disable=broad-except
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)