def test_streaming_metric_on_single_image(self):
offset = 256 * 256
instance_class_map = {
0: 0,
47: 1,
97: 1,
133: 1,
150: 1,
174: 1,
198: 2,
215: 1,
244: 1,
255: 1,
}
gt_instances, gt_classes = test_utils.panoptic_segmentation_with_class_map(
'team_gt_instance.png', instance_class_map)
pred_classes = test_utils.read_segmentation_with_rgb_color_map(
'team_pred_class.png', _CLASS_COLOR_MAP)
pred_instances = test_utils.read_test_image(
'team_pred_instance.png', mode='L')
gt_class_tensor = tf.placeholder(tf.uint16)
gt_instance_tensor = tf.placeholder(tf.uint16)
pred_class_tensor = tf.placeholder(tf.uint16)
pred_instance_tensor = tf.placeholder(tf.uint16)
qualities, update_pq = streaming_metrics.streaming_panoptic_quality(
gt_class_tensor,
gt_instance_tensor,
pred_class_tensor,
pred_instance_tensor,
num_classes=3,
max_instances_per_category=256,
ignored_label=0,
offset=offset)
pq, sq, rq, total_tp, total_fn, total_fp = tf.unstack(qualities, 6, axis=0)
feed_dict = {
gt_class_tensor: gt_classes,
gt_instance_tensor: gt_instances,
pred_class_tensor: pred_classes,
pred_instance_tensor: pred_instances
}
with self.session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(update_pq, feed_dict=feed_dict)
(result_pq, result_sq, result_rq, result_total_tp, result_total_fn,
result_total_fp) = sess.run([pq, sq, rq, total_tp, total_fn, total_fp],
feed_dict=feed_dict)
np.testing.assert_array_almost_equal(
result_pq, [2.06104, 0.7024, 0.54069], decimal=4)
np.testing.assert_array_almost_equal(
result_sq, [2.06104, 0.7526, 0.54069], decimal=4)
np.testing.assert_array_almost_equal(result_rq, [1., 0.9333, 1.], decimal=4)
np.testing.assert_array_almost_equal(
result_total_tp, [1., 7., 1.], decimal=4)
np.testing.assert_array_almost_equal(
result_total_fn, [0., 1., 0.], decimal=4)
np.testing.assert_array_almost_equal(
result_total_fp, [0., 0., 0.], decimal=4)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
dataset = data_generator.Dataset(
dataset_name=FLAGS.dataset,
split_name=FLAGS.eval_split,
dataset_dir=FLAGS.dataset_dir,
batch_size=FLAGS.eval_batch_size,
crop_size=[int(sz) for sz in FLAGS.eval_crop_size],
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
model_variant=FLAGS.model_variant,
num_readers=2,
is_training=False,
should_shuffle=False,
should_repeat=False)
tf.gfile.MakeDirs(FLAGS.eval_logdir)
tf.logging.info('Evaluating on %s set', FLAGS.eval_split)
with tf.Graph().as_default():
samples = dataset.get_one_shot_iterator().get_next()
model_options = common.ModelOptions(
outputs_to_num_classes={
common.OUTPUT_TYPE: dataset.num_of_classes,
common.INSTANCE: 1,
common.OFFSET: 2
},
crop_size=[int(sz) for sz in FLAGS.eval_crop_size],
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
# Set shape in order for tf.contrib.tfprof.model_analyzer to work properly.
samples[common.IMAGE].set_shape([
FLAGS.eval_batch_size,
int(FLAGS.eval_crop_size[0]),
int(FLAGS.eval_crop_size[1]), 3
])
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.')
if FLAGS.quantize_delay_step >= 0:
raise ValueError(
'Quantize mode is not supported with multi-scale test.')
predictions_semantic = predictions[common.OUTPUT_TYPE]
predictions_center_points = predictions[common.INSTANCE]
predictions_offset_vectors = predictions[common.OFFSET]
# tf Non-maxima Suppression
# Pooling based NMS for Pooling Instance Centers
# Filtering predictions that are less than 0.1
instance_prediction = generate_instance_segmentation(
predictions_semantic, predictions_center_points,
predictions_offset_vectors)
category_prediction = tf.squeeze(predictions_semantic)
category_label = tf.squeeze(samples[common.LABEL][0])
not_ignore_mask = tf.not_equal(category_label, 255)
category_label = tf.cast(
category_label * tf.cast(not_ignore_mask, tf.int32), tf.int32)
instance_label = tf.squeeze(samples[common.LABEL_INSTANCE_IDS][0])
category_prediction = category_prediction * tf.cast(
not_ignore_mask, tf.int64)
instance_prediction = instance_prediction * tf.cast(
not_ignore_mask, tf.int64)
# Define the evaluation metric.
metric_map = {}
metric_map[
'panoptic_quality'] = streaming_metrics.streaming_panoptic_quality(
category_label,
instance_label,
category_prediction,
instance_prediction,
num_classes=19,
max_instances_per_category=256,
ignored_label=255,
offset=256 * 256)
metric_map[
'parsing_covering'] = streaming_metrics.streaming_parsing_covering(
category_label,
instance_label,
category_prediction,
instance_prediction,
num_classes=19,
max_instances_per_category=256,
ignored_label=255,
offset=256 * 256,
normalize_by_image_size=True)
metrics_to_values, metrics_to_updates = slim.metrics.aggregate_metric_map(
metric_map)
summary_ops = []
for metric_name, metric_value in metrics_to_values.iteritems():
if metric_name == 'panoptic_quality':
[pq, sq, rq, total_tp, total_fn,
total_fp] = tf.unstack(metric_value, 6, axis=0)
panoptic_metrics = {
# Panoptic quality.
'pq': pq,
# Segmentation quality.
'sq': sq,
# Recognition quality.
'rq': rq,
# Total true positives.
'total_tp': total_tp,
# Total false negatives.
'total_fn': total_fn,
# Total false positives.
'total_fp': total_fp,
}
# Find the valid classes that will be used for evaluation. We will
# ignore the `ignore_label` class and other classes which have (tp + fn
# + fp) equal to 0.
valid_classes = tf.logical_and(
tf.not_equal(tf.range(0, dataset.num_of_classes),
dataset.ignore_label),
tf.not_equal(total_tp + total_fn + total_fp, 0))
for target_metric, target_value in panoptic_metrics.iteritems(
):
output_metric_name = '{}_{}'.format(
metric_name, target_metric)
op = tf.summary.scalar(
output_metric_name,
tf.reduce_mean(
tf.boolean_mask(target_value, valid_classes)))
op = tf.Print(op, [target_value],
output_metric_name + '_classwise: ',
summarize=dataset.num_of_classes)
op = tf.Print(op, [
tf.reduce_mean(
tf.boolean_mask(target_value, valid_classes))
],
output_metric_name + '_mean: ',
summarize=1)
summary_ops.append(op)
elif metric_name == 'parsing_covering':
[
per_class_covering, total_per_class_weighted_ious,
total_per_class_gt_areas
] = tf.unstack(metric_value, 3, axis=0)
# Find the valid classes that will be used for evaluation. We will
# ignore the `void_label` class and other classes which have
# total_per_class_weighted_ious + total_per_class_gt_areas equal to 0.
valid_classes = tf.logical_and(
tf.not_equal(tf.range(0, dataset.num_of_classes),
dataset.ignore_label),
tf.not_equal(
total_per_class_weighted_ious +
total_per_class_gt_areas, 0))
op = tf.summary.scalar(
metric_name,
tf.reduce_mean(
tf.boolean_mask(per_class_covering, valid_classes)))
op = tf.Print(op, [per_class_covering],
metric_name + '_classwise: ',
summarize=dataset.num_of_classes)
op = tf.Print(op, [
tf.reduce_mean(
tf.boolean_mask(per_class_covering, valid_classes))
],
metric_name + '_mean: ',
summarize=1)
summary_ops.append(op)
else:
raise ValueError('The metric_name "%s" is not supported.' %
metric_name)
num_eval_iters = None
if FLAGS.max_number_of_evaluations > 0:
num_eval_iters = FLAGS.max_number_of_evaluations
if FLAGS.quantize_delay_step >= 0:
contrib_quantize.create_eval_graph()
contrib_tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=contrib_tfprof.model_analyzer.
TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
contrib_tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=contrib_tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
metric_values = slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
logdir=FLAGS.eval_logdir,
num_evals=20,
eval_op=metrics_to_updates.values(),
final_op=metrics_to_values.values(),
summary_op=tf.summary.merge(summary_ops),
max_number_of_evaluations=FLAGS.max_number_of_evaluations,
eval_interval_secs=FLAGS.eval_interval_secs)
def test_streaming_metric_on_multiple_images(self):
num_classes = 7
offset = 256 * 256
bird_gt_instance_class_map = {
92: 5,
176: 3,
255: 4,
}
cat_gt_instance_class_map = {
0: 0,
255: 6,
}
team_gt_instance_class_map = {
0: 0,
47: 1,
97: 1,
133: 1,
150: 1,
174: 1,
198: 2,
215: 1,
244: 1,
255: 1,
}
test_image = collections.namedtuple(
'TestImage',
['gt_class_map', 'gt_path', 'pred_inst_path', 'pred_class_path'])
test_images = [
test_image(bird_gt_instance_class_map, 'bird_gt.png',
'bird_pred_instance.png', 'bird_pred_class.png'),
test_image(cat_gt_instance_class_map, 'cat_gt.png',
'cat_pred_instance.png', 'cat_pred_class.png'),
test_image(team_gt_instance_class_map, 'team_gt_instance.png',
'team_pred_instance.png', 'team_pred_class.png'),
]
gt_classes = []
gt_instances = []
pred_classes = []
pred_instances = []
for test_image in test_images:
(image_gt_instances,
image_gt_classes) = test_utils.panoptic_segmentation_with_class_map(
test_image.gt_path, test_image.gt_class_map)
gt_classes.append(image_gt_classes)
gt_instances.append(image_gt_instances)
pred_classes.append(
test_utils.read_segmentation_with_rgb_color_map(
test_image.pred_class_path, _CLASS_COLOR_MAP))
pred_instances.append(
test_utils.read_test_image(test_image.pred_inst_path, mode='L'))
gt_class_tensor = tf.placeholder(tf.uint16)
gt_instance_tensor = tf.placeholder(tf.uint16)
pred_class_tensor = tf.placeholder(tf.uint16)
pred_instance_tensor = tf.placeholder(tf.uint16)
qualities, update_pq = streaming_metrics.streaming_panoptic_quality(
gt_class_tensor,
gt_instance_tensor,
pred_class_tensor,
pred_instance_tensor,
num_classes=num_classes,
max_instances_per_category=256,
ignored_label=0,
offset=offset)
pq, sq, rq, total_tp, total_fn, total_fp = tf.unstack(qualities, 6, axis=0)
with self.session() as sess:
sess.run(tf.local_variables_initializer())
for pred_class, pred_instance, gt_class, gt_instance in six.moves.zip(
pred_classes, pred_instances, gt_classes, gt_instances):
sess.run(
update_pq,
feed_dict={
gt_class_tensor: gt_class,
gt_instance_tensor: gt_instance,
pred_class_tensor: pred_class,
pred_instance_tensor: pred_instance
})
(result_pq, result_sq, result_rq, result_total_tp, result_total_fn,
result_total_fp) = sess.run(
[pq, sq, rq, total_tp, total_fn, total_fp],
feed_dict={
gt_class_tensor: 0,
gt_instance_tensor: 0,
pred_class_tensor: 0,
pred_instance_tensor: 0
})
np.testing.assert_array_almost_equal(
result_pq,
[4.3107, 0.7024, 0.54069, 0.745353, 0.85768, 0.99107, 0.77410],
decimal=4)
np.testing.assert_array_almost_equal(
result_sq, [5.3883, 0.7526, 0.5407, 0.7454, 0.8577, 0.9911, 0.7741],
decimal=4)
np.testing.assert_array_almost_equal(
result_rq, [0.8, 0.9333, 1., 1., 1., 1., 1.], decimal=4)
np.testing.assert_array_almost_equal(
result_total_tp, [2., 7., 1., 1., 1., 1., 1.], decimal=4)
np.testing.assert_array_almost_equal(
result_total_fn, [0., 1., 0., 0., 0., 0., 0.], decimal=4)
np.testing.assert_array_almost_equal(
result_total_fp, [1., 0., 0., 0., 0., 0., 0.], decimal=4)