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eval_coco_format_test.py
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# 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.
# ==============================================================================
"""Tests for eval_coco_format script."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import flags
from absl.testing import absltest
import evaluation as panopticapi_eval
from deeplab.evaluation import eval_coco_format
_TEST_DIR = 'deeplab/evaluation/testdata'
FLAGS = flags.FLAGS
class EvalCocoFormatTest(absltest.TestCase):
def test_compare_pq_with_reference_eval(self):
sample_data_dir = os.path.join(_TEST_DIR)
gt_json_file = os.path.join(sample_data_dir, 'coco_gt.json')
gt_folder = os.path.join(sample_data_dir, 'coco_gt')
pred_json_file = os.path.join(sample_data_dir, 'coco_pred.json')
pred_folder = os.path.join(sample_data_dir, 'coco_pred')
panopticapi_results = panopticapi_eval.pq_compute(
gt_json_file, pred_json_file, gt_folder, pred_folder)
deeplab_results = eval_coco_format.eval_coco_format(
gt_json_file,
pred_json_file,
gt_folder,
pred_folder,
metric='pq',
num_categories=7,
ignored_label=0,
max_instances_per_category=256,
intersection_offset=(256 * 256))
self.assertCountEqual(
list(deeplab_results.keys()), ['All', 'Things', 'Stuff'])
for cat_group in ['All', 'Things', 'Stuff']:
self.assertCountEqual(deeplab_results[cat_group], ['pq', 'sq', 'rq', 'n'])
for metric in ['pq', 'sq', 'rq', 'n']:
self.assertAlmostEqual(deeplab_results[cat_group][metric],
panopticapi_results[cat_group][metric])
def test_compare_pc_with_golden_value(self):
sample_data_dir = os.path.join(_TEST_DIR)
gt_json_file = os.path.join(sample_data_dir, 'coco_gt.json')
gt_folder = os.path.join(sample_data_dir, 'coco_gt')
pred_json_file = os.path.join(sample_data_dir, 'coco_pred.json')
pred_folder = os.path.join(sample_data_dir, 'coco_pred')
deeplab_results = eval_coco_format.eval_coco_format(
gt_json_file,
pred_json_file,
gt_folder,
pred_folder,
metric='pc',
num_categories=7,
ignored_label=0,
max_instances_per_category=256,
intersection_offset=(256 * 256),
normalize_by_image_size=False)
self.assertCountEqual(
list(deeplab_results.keys()), ['All', 'Things', 'Stuff'])
for cat_group in ['All', 'Things', 'Stuff']:
self.assertCountEqual(deeplab_results[cat_group], ['pc', 'n'])
self.assertAlmostEqual(deeplab_results['All']['pc'], 0.68210561)
self.assertEqual(deeplab_results['All']['n'], 6)
self.assertAlmostEqual(deeplab_results['Things']['pc'], 0.5890529)
self.assertEqual(deeplab_results['Things']['n'], 4)
self.assertAlmostEqual(deeplab_results['Stuff']['pc'], 0.86821097)
self.assertEqual(deeplab_results['Stuff']['n'], 2)
def test_compare_pc_with_golden_value_normalize_by_size(self):
sample_data_dir = os.path.join(_TEST_DIR)
gt_json_file = os.path.join(sample_data_dir, 'coco_gt.json')
gt_folder = os.path.join(sample_data_dir, 'coco_gt')
pred_json_file = os.path.join(sample_data_dir, 'coco_pred.json')
pred_folder = os.path.join(sample_data_dir, 'coco_pred')
deeplab_results = eval_coco_format.eval_coco_format(
gt_json_file,
pred_json_file,
gt_folder,
pred_folder,
metric='pc',
num_categories=7,
ignored_label=0,
max_instances_per_category=256,
intersection_offset=(256 * 256),
normalize_by_image_size=True)
self.assertCountEqual(
list(deeplab_results.keys()), ['All', 'Things', 'Stuff'])
self.assertAlmostEqual(deeplab_results['All']['pc'], 0.68214908840)
def test_pc_with_multiple_workers(self):
sample_data_dir = os.path.join(_TEST_DIR)
gt_json_file = os.path.join(sample_data_dir, 'coco_gt.json')
gt_folder = os.path.join(sample_data_dir, 'coco_gt')
pred_json_file = os.path.join(sample_data_dir, 'coco_pred.json')
pred_folder = os.path.join(sample_data_dir, 'coco_pred')
deeplab_results = eval_coco_format.eval_coco_format(
gt_json_file,
pred_json_file,
gt_folder,
pred_folder,
metric='pc',
num_categories=7,
ignored_label=0,
max_instances_per_category=256,
intersection_offset=(256 * 256),
num_workers=3,
normalize_by_image_size=False)
self.assertCountEqual(
list(deeplab_results.keys()), ['All', 'Things', 'Stuff'])
self.assertAlmostEqual(deeplab_results['All']['pc'], 0.68210561668)
if __name__ == '__main__':
absltest.main()