parsing_covering.py

# Lint as: python2, python3
# Copyright 2019 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Implementation of the Parsing Covering metric.

Parsing Covering is a region-based metric for evaluating the task of
image parsing, aka panoptic segmentation.

Please see the paper for details:
"DeeperLab: Single-Shot Image Parser", Tien-Ju Yang, Maxwell D. Collins,
Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze,
George Papandreou, Liang-Chieh Chen. arXiv: 1902.05093, 2019.
"""

from __future__ importabsolute_import
from __future__ importdivision
from __future__ importprint_function

importcollections

importnumpyasnp
importprettytable
importsix

fromdeeplab.evaluationimportbase_metric


classParsingCovering(base_metric.SegmentationMetric):
r"""Metric class for Parsing Covering.

 Computes segmentation covering metric introduced in (Arbelaez, et al., 2010)
 with extension to handle multi-class semantic labels (a.k.a. parsing
 covering). Specifically, segmentation covering (SC) is defined in Eq. (8) in
 (Arbelaez et al., 2010) as:

 SC(c) = \sum_{R\in S}(|R| * \max_{R'\in S'}O(R,R')) / \sum_{R\in S}|R|,

 where S are the groundtruth instance regions and S' are the predicted
 instance regions. The parsing covering is simply:

 PC = \sum_{c=1}^{C}SC(c) / C,

 where C is the number of classes.
 """

def__init__(self,
num_categories,
ignored_label,
max_instances_per_category,
offset,
normalize_by_image_size=True):
"""Initialization for ParsingCovering.

 Args:
 num_categories: The number of segmentation categories (or "classes" in the
 dataset.
 ignored_label: A category id that is ignored in evaluation, e.g. the void
 label as defined in COCO panoptic segmentation dataset.
 max_instances_per_category: The maximum number of instances for each
 category. Used in ensuring unique instance labels.
 offset: The maximum number of unique labels. This is used, by multiplying
 the ground-truth labels, to generate unique ids for individual regions
 of overlap between groundtruth and predicted segments.
 normalize_by_image_size: Whether to normalize groundtruth instance region
 areas by image size. If True, groundtruth instance areas and weighted
 IoUs will be divided by the size of the corresponding image before
 accumulated across the dataset.
 """
super(ParsingCovering, self).__init__(num_categories, ignored_label,
max_instances_per_category, offset)
self.normalize_by_image_size=normalize_by_image_size

defcompare_and_accumulate(
self, groundtruth_category_array, groundtruth_instance_array,
predicted_category_array, predicted_instance_array):
"""See base class."""
# Allocate intermediate data structures.
max_ious=np.zeros([self.num_categories, self.max_instances_per_category],
dtype=np.float64)
gt_areas=np.zeros([self.num_categories, self.max_instances_per_category],
dtype=np.float64)
pred_areas=np.zeros(
 [self.num_categories, self.max_instances_per_category],
dtype=np.float64)
# This is a dictionary in the format:
# {(category, gt_instance): [(pred_instance, intersection_area)]}.
intersections=collections.defaultdict(list)

# First, combine the category and instance labels so that every unique
# value for (category, instance) is assigned a unique integer label.
pred_segment_id=self._naively_combine_labels(predicted_category_array,
predicted_instance_array)
gt_segment_id=self._naively_combine_labels(groundtruth_category_array,
groundtruth_instance_array)

# Next, combine the groundtruth and predicted labels. Dividing up the pixels
# based on which groundtruth segment and which predicted segment they belong
# to, this will assign a different 32-bit integer label to each choice
# of (groundtruth segment, predicted segment), encoded as
# gt_segment_id * offset + pred_segment_id.
intersection_id_array= (
gt_segment_id.astype(np.uint32) *self.offset+
pred_segment_id.astype(np.uint32))

# For every combination of (groundtruth segment, predicted segment) with a
# non-empty intersection, this counts the number of pixels in that
# intersection.
intersection_ids, intersection_areas=np.unique(
intersection_id_array, return_counts=True)

# Find areas of all groundtruth and predicted instances, as well as of their
# intersections.
forintersection_id, intersection_areainsix.moves.zip(
intersection_ids, intersection_areas):
gt_segment_id=intersection_id//self.offset
gt_category=gt_segment_id//self.max_instances_per_category
ifgt_category==self.ignored_label:
continue
gt_instance=gt_segment_id%self.max_instances_per_category
gt_areas[gt_category, gt_instance] +=intersection_area

pred_segment_id=intersection_id%self.offset
pred_category=pred_segment_id//self.max_instances_per_category
pred_instance=pred_segment_id%self.max_instances_per_category
pred_areas[pred_category, pred_instance] +=intersection_area
ifpred_category!=gt_category:
continue

intersections[gt_category, gt_instance].append((pred_instance,
intersection_area))

# Find maximum IoU for every groundtruth instance.
forgt_label, instance_intersectionsinsix.iteritems(intersections):
category, gt_instance=gt_label
gt_area=gt_areas[category, gt_instance]
ious= []
forpred_instance, intersection_areaininstance_intersections:
pred_area=pred_areas[category, pred_instance]
union=gt_area+pred_area-intersection_area
ious.append(intersection_area/union)
max_ious[category, gt_instance] =max(ious)

# Normalize groundtruth instance areas by image size if necessary.
ifself.normalize_by_image_size:
gt_areas/=groundtruth_category_array.size

# Compute per-class weighted IoUs and areas summed over all groundtruth
# instances.
self.weighted_iou_per_class+=np.sum(max_ious*gt_areas, axis=-1)
self.gt_area_per_class+=np.sum(gt_areas, axis=-1)

returnself.result()

defresult_per_category(self):
"""See base class."""
returnbase_metric.realdiv_maybe_zero(self.weighted_iou_per_class,
self.gt_area_per_class)

def_valid_categories(self):
"""Categories with a "valid" value for the metric, have > 0 instances.

 We will ignore the `ignore_label` class and other classes which have
 groundtruth area of 0.

 Returns:
 Boolean array of shape `[num_categories]`.
 """
valid_categories=np.not_equal(self.gt_area_per_class, 0)
ifself.ignored_label>=0andself.ignored_label<self.num_categories:
valid_categories[self.ignored_label] =False
returnvalid_categories

defdetailed_results(self, is_thing=None):
"""See base class."""
valid_categories=self._valid_categories()

# If known, break down which categories are valid _and_ things/stuff.
category_sets=collections.OrderedDict()
category_sets['All'] =valid_categories
ifis_thingisnotNone:
category_sets['Things'] =np.logical_and(valid_categories, is_thing)
category_sets['Stuff'] =np.logical_and(valid_categories,
np.logical_not(is_thing))

covering_per_class=self.result_per_category()
results= {}
forcategory_set_name, in_category_setinsix.iteritems(category_sets):
ifnp.any(in_category_set):
results[category_set_name] = {
'pc': np.mean(covering_per_class[in_category_set]),
# The number of valid categories in this subset.
'n': np.sum(in_category_set.astype(np.int32)),
 }
else:
results[category_set_name] = {'pc': 0, 'n': 0}

returnresults

defprint_detailed_results(self, is_thing=None, print_digits=3):
"""See base class."""
results=self.detailed_results(is_thing=is_thing)

tab=prettytable.PrettyTable()

tab.add_column('', [], align='l')
forfieldnamein ['PC', 'N']:
tab.add_column(fieldname, [], align='r')

forcategory_set, subset_resultsinsix.iteritems(results):
data_cols= [
round(subset_results['pc'], print_digits) *100, subset_results['n']
 ]
tab.add_row([category_set] +data_cols)

print(tab)

defresult(self):
"""See base class."""
covering_per_class=self.result_per_category()
valid_categories=self._valid_categories()
ifnotnp.any(valid_categories):
return0.
returnnp.mean(covering_per_class[valid_categories])

defmerge(self, other_instance):
"""See base class."""
self.weighted_iou_per_class+=other_instance.weighted_iou_per_class
self.gt_area_per_class+=other_instance.gt_area_per_class

defreset(self):
"""See base class."""
self.weighted_iou_per_class=np.zeros(
self.num_categories, dtype=np.float64)
self.gt_area_per_class=np.zeros(self.num_categories, dtype=np.float64)