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evaluate.py
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evaluate.py
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"""Evaluation script for the DeepLab model.
See model.py for more details and usage.
"""
import math
import six
import tensorflow as tf
from tensorflow.contrib import slim
import common
import model
from dataset import segmentation_dataset
from utils import input_generator
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('master', '', 'BNS name of the tensorflow server')
# Settings for log directories.
flags.DEFINE_string('eval_logdir', None, 'Where to write the event logs.')
flags.DEFINE_string('checkpoint_dir', None, 'Directory of model checkpoints.')
# Settings for evaluating the model.
flags.DEFINE_integer('eval_batch_size', 1,
'The number of images in each batch during evaluation.')
flags.DEFINE_multi_integer('eval_crop_size', [769, 769],
'Image crop size [height, width] for evaluation.')
flags.DEFINE_integer('eval_interval_secs', 60 * 5,
'How often (in seconds) to run evaluation.')
# For `mobilenet_v2` and `shufflenet_v2`, use None.
flags.DEFINE_multi_integer('atrous_rates', None,
'Atrous rates for atrous spatial pyramid pooling.')
flags.DEFINE_integer('output_stride', 16,
'The ratio of input to output spatial resolution.')
# Change to [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] for multi-scale test.
flags.DEFINE_multi_float('eval_scales', [1.0],
'The scales to resize images for evaluation.')
# Change to True for adding flipped images during test.
flags.DEFINE_bool('add_flipped_images', False,
'Add flipped images for evaluation or not.')
# Dataset settings.
flags.DEFINE_string('dataset', 'cityscapes',
'Name of the segmentation dataset.')
flags.DEFINE_string('eval_split', 'val',
'Which split of the dataset used for evaluation')
flags.DEFINE_string('dataset_dir', None, 'Where the dataset reside.')
flags.DEFINE_integer('max_number_of_evaluations', 0,
'Maximum number of eval iterations. Will loop '
'indefinitely upon nonpositive values.')
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Get dataset-dependent information.
dataset = segmentation_dataset.get_dataset(
FLAGS.dataset, FLAGS.eval_split, dataset_dir=FLAGS.dataset_dir)
tf.gfile.MakeDirs(FLAGS.eval_logdir)
tf.logging.info('Evaluating on %s set', FLAGS.eval_split)
with tf.Graph().as_default():
samples = input_generator.get(
dataset,
FLAGS.eval_crop_size,
FLAGS.eval_batch_size,
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
dataset_split=FLAGS.eval_split,
is_training=False,
model_variant=FLAGS.model_variant)
model_options = common.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: dataset.num_classes},
crop_size=FLAGS.eval_crop_size,
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
if tuple(FLAGS.eval_scales) == (1.0,):
tf.logging.info('Performing single-scale test.')
predictions = model.predict_labels(samples[common.IMAGE], model_options,
image_pyramid=FLAGS.image_pyramid)
else:
tf.logging.info('Performing multi-scale test.')
predictions = model.predict_labels_multi_scale(
samples[common.IMAGE],
model_options=model_options,
eval_scales=FLAGS.eval_scales,
add_flipped_images=FLAGS.add_flipped_images)
predictions = predictions[common.OUTPUT_TYPE]
predictions = tf.reshape(predictions, shape=[-1])
labels = tf.reshape(samples[common.LABEL], shape=[-1])
weights = tf.cast(tf.not_equal(labels, dataset.ignore_label), tf.float32)
# Set ignore_label regions to label 0, because metrics.mean_iou requires
# range of labels = [0, dataset.num_classes). Note the ignore_label regions
# are not evaluated since the corresponding regions contain weights = 0.
labels = tf.where(
tf.equal(labels, dataset.ignore_label), tf.zeros_like(labels), labels)
predictions_tag = 'miou'
for eval_scale in FLAGS.eval_scales:
predictions_tag += '_' + str(eval_scale)
if FLAGS.add_flipped_images:
predictions_tag += '_flipped'
# Define the evaluation metric.
metric_map = {}
metric_map[predictions_tag] = tf.metrics.mean_iou(
predictions, labels, dataset.num_classes, weights=weights)
metrics_to_values, metrics_to_updates = (
tf.contrib.metrics.aggregate_metric_map(metric_map))
for metric_name, metric_value in six.iteritems(metrics_to_values):
slim.summaries.add_scalar_summary(
metric_value, metric_name, print_summary=True)
num_batches = int(
math.ceil(dataset.num_samples / float(FLAGS.eval_batch_size)))
tf.logging.info('Eval num images %d', dataset.num_samples)
tf.logging.info('Eval batch size %d and num batch %d',
FLAGS.eval_batch_size, num_batches)
num_eval_iters = None
if FLAGS.max_number_of_evaluations > 0:
num_eval_iters = FLAGS.max_number_of_evaluations
# Soft placement allows placing on CPU ops without GPU implementation.
session_config = tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False)
session_config.gpu_options.allow_growth = True
slim.evaluation.evaluation_loop(
session_config=session_config,
master=FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
logdir=FLAGS.eval_logdir,
num_evals=num_batches,
eval_op=list(metrics_to_updates.values()),
max_number_of_evaluations=num_eval_iters,
eval_interval_secs=FLAGS.eval_interval_secs)
if __name__ == '__main__':
flags.mark_flag_as_required('checkpoint_dir')
flags.mark_flag_as_required('eval_logdir')
flags.mark_flag_as_required('dataset_dir')
tf.app.run()