research/object_detection/eval_util_test.py
# Copyright 2017 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_util."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
from absl.testing import parameterized
import numpy as np
import six
from six.moves import range
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
from object_detection import eval_util
from object_detection.core import standard_fields as fields
from object_detection.metrics import coco_evaluation
from object_detection.protos import eval_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
def _get_categories_list(self):
return [{'id': 1, 'name': 'person'},
{'id': 2, 'name': 'dog'},
{'id': 3, 'name': 'cat'}]
def _get_categories_list_with_keypoints(self):
return [{
'id': 1,
'name': 'person',
'keypoints': {
'left_eye': 0,
'right_eye': 3
}
}, {
'id': 2,
'name': 'dog',
'keypoints': {
'tail_start': 1,
'mouth': 2
}
}, {
'id': 3,
'name': 'cat'
}]
def _make_evaluation_dict(self,
resized_groundtruth_masks=False,
batch_size=1,
max_gt_boxes=None,
scale_to_absolute=False):
input_data_fields = fields.InputDataFields
detection_fields = fields.DetectionResultFields
image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
if batch_size == 1:
key = tf.constant('image1')
else:
key = tf.constant([str(i) for i in range(batch_size)])
detection_boxes = tf.tile(tf.constant([[[0., 0., 1., 1.]]]),
multiples=[batch_size, 1, 1])
detection_scores = tf.tile(tf.constant([[0.8]]), multiples=[batch_size, 1])
detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
detection_masks = tf.tile(tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32),
multiples=[batch_size, 1, 1, 1])
num_detections = tf.ones([batch_size])
groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
groundtruth_classes = tf.constant([1])
groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
original_image_spatial_shapes = tf.constant([[20, 20]], dtype=tf.int32)
groundtruth_keypoints = tf.constant([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]])
if resized_groundtruth_masks:
groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
if batch_size > 1:
groundtruth_boxes = tf.tile(tf.expand_dims(groundtruth_boxes, 0),
multiples=[batch_size, 1, 1])
groundtruth_classes = tf.tile(tf.expand_dims(groundtruth_classes, 0),
multiples=[batch_size, 1])
groundtruth_instance_masks = tf.tile(
tf.expand_dims(groundtruth_instance_masks, 0),
multiples=[batch_size, 1, 1, 1])
groundtruth_keypoints = tf.tile(
tf.expand_dims(groundtruth_keypoints, 0),
multiples=[batch_size, 1, 1])
original_image_spatial_shapes = tf.tile(original_image_spatial_shapes,
multiples=[batch_size, 1])
detections = {
detection_fields.detection_boxes: detection_boxes,
detection_fields.detection_scores: detection_scores,
detection_fields.detection_classes: detection_classes,
detection_fields.detection_masks: detection_masks,
detection_fields.num_detections: num_detections
}
groundtruth = {
input_data_fields.groundtruth_boxes: groundtruth_boxes,
input_data_fields.groundtruth_classes: groundtruth_classes,
input_data_fields.groundtruth_keypoints: groundtruth_keypoints,
input_data_fields.groundtruth_instance_masks:
groundtruth_instance_masks,
input_data_fields.original_image_spatial_shape:
original_image_spatial_shapes
}
if batch_size > 1:
return eval_util.result_dict_for_batched_example(
image, key, detections, groundtruth,
scale_to_absolute=scale_to_absolute,
max_gt_boxes=max_gt_boxes)
else:
return eval_util.result_dict_for_single_example(
image, key, detections, groundtruth,
scale_to_absolute=scale_to_absolute)
@parameterized.parameters(
{'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': True},
{'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': True},
{'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': False},
{'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': False}
)
@unittest.skipIf(tf_version.is_tf2(), 'Only compatible with TF1.X')
def test_get_eval_metric_ops_for_coco_detections(self, batch_size=1,
max_gt_boxes=None,
scale_to_absolute=False):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(['coco_detection_metrics'])
categories = self._get_categories_list()
eval_dict = self._make_evaluation_dict(batch_size=batch_size,
max_gt_boxes=max_gt_boxes,
scale_to_absolute=scale_to_absolute)
metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
eval_config, categories, eval_dict)
_, update_op = metric_ops['DetectionBoxes_Precision/mAP']
with self.test_session() as sess:
metrics = {}
for key, (value_op, _) in six.iteritems(metric_ops):
metrics[key] = value_op
sess.run(update_op)
metrics = sess.run(metrics)
self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
@parameterized.parameters(
{'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': True},
{'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': True},
{'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': False},
{'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': False}
)
@unittest.skipIf(tf_version.is_tf2(), 'Only compatible with TF1.X')
def test_get_eval_metric_ops_for_coco_detections_and_masks(
self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(
['coco_detection_metrics', 'coco_mask_metrics'])
categories = self._get_categories_list()
eval_dict = self._make_evaluation_dict(batch_size=batch_size,
max_gt_boxes=max_gt_boxes,
scale_to_absolute=scale_to_absolute)
metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
eval_config, categories, eval_dict)
_, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
_, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']
with self.test_session() as sess:
metrics = {}
for key, (value_op, _) in six.iteritems(metric_ops):
metrics[key] = value_op
sess.run(update_op_boxes)
sess.run(update_op_masks)
metrics = sess.run(metrics)
self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
@parameterized.parameters(
{'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': True},
{'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': True},
{'batch_size': 1, 'max_gt_boxes': None, 'scale_to_absolute': False},
{'batch_size': 8, 'max_gt_boxes': [1], 'scale_to_absolute': False}
)
@unittest.skipIf(tf_version.is_tf2(), 'Only compatible with TF1.X')
def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(
self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(
['coco_detection_metrics', 'coco_mask_metrics'])
categories = self._get_categories_list()
eval_dict = self._make_evaluation_dict(batch_size=batch_size,
max_gt_boxes=max_gt_boxes,
scale_to_absolute=scale_to_absolute,
resized_groundtruth_masks=True)
metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
eval_config, categories, eval_dict)
_, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
_, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']
with self.test_session() as sess:
metrics = {}
for key, (value_op, _) in six.iteritems(metric_ops):
metrics[key] = value_op
sess.run(update_op_boxes)
sess.run(update_op_masks)
metrics = sess.run(metrics)
self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
@unittest.skipIf(tf_version.is_tf2(), 'Only compatible with TF1.X')
def test_get_eval_metric_ops_raises_error_with_unsupported_metric(self):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(['unsupported_metric'])
categories = self._get_categories_list()
eval_dict = self._make_evaluation_dict()
with self.assertRaises(ValueError):
eval_util.get_eval_metric_ops_for_evaluators(
eval_config, categories, eval_dict)
def test_get_eval_metric_ops_for_evaluators(self):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend([
'coco_detection_metrics', 'coco_mask_metrics',
'precision_at_recall_detection_metrics'
])
eval_config.include_metrics_per_category = True
eval_config.recall_lower_bound = 0.2
eval_config.recall_upper_bound = 0.6
evaluator_options = eval_util.evaluator_options_from_eval_config(
eval_config)
self.assertTrue(evaluator_options['coco_detection_metrics']
['include_metrics_per_category'])
self.assertFalse(evaluator_options['coco_detection_metrics']
['skip_predictions_for_unlabeled_class'])
self.assertTrue(
evaluator_options['coco_mask_metrics']['include_metrics_per_category'])
self.assertAlmostEqual(
evaluator_options['precision_at_recall_detection_metrics']
['recall_lower_bound'], eval_config.recall_lower_bound)
self.assertAlmostEqual(
evaluator_options['precision_at_recall_detection_metrics']
['recall_upper_bound'], eval_config.recall_upper_bound)
self.assertFalse(evaluator_options['precision_at_recall_detection_metrics']
['skip_predictions_for_unlabeled_class'])
def test_get_evaluator_with_evaluator_options(self):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(
['coco_detection_metrics', 'precision_at_recall_detection_metrics'])
eval_config.include_metrics_per_category = True
eval_config.skip_predictions_for_unlabeled_class = True
eval_config.recall_lower_bound = 0.2
eval_config.recall_upper_bound = 0.6
categories = self._get_categories_list()
evaluator_options = eval_util.evaluator_options_from_eval_config(
eval_config)
evaluator = eval_util.get_evaluators(eval_config, categories,
evaluator_options)
self.assertTrue(evaluator[0]._include_metrics_per_category)
self.assertTrue(evaluator[0]._skip_predictions_for_unlabeled_class)
self.assertTrue(evaluator[1]._skip_predictions_for_unlabeled_class)
self.assertAlmostEqual(evaluator[1]._recall_lower_bound,
eval_config.recall_lower_bound)
self.assertAlmostEqual(evaluator[1]._recall_upper_bound,
eval_config.recall_upper_bound)
def test_get_evaluator_with_no_evaluator_options(self):
eval_config = eval_pb2.EvalConfig()
eval_config.metrics_set.extend(
['coco_detection_metrics', 'precision_at_recall_detection_metrics'])
eval_config.include_metrics_per_category = True
eval_config.recall_lower_bound = 0.2
eval_config.recall_upper_bound = 0.6
categories = self._get_categories_list()
evaluator = eval_util.get_evaluators(
eval_config, categories, evaluator_options=None)
# Even though we are setting eval_config.include_metrics_per_category = True
# and bounds on recall, these options are never passed into the
# DetectionEvaluator constructor (via `evaluator_options`).
self.assertFalse(evaluator[0]._include_metrics_per_category)
self.assertAlmostEqual(evaluator[1]._recall_lower_bound, 0.0)
self.assertAlmostEqual(evaluator[1]._recall_upper_bound, 1.0)
def test_get_evaluator_with_keypoint_metrics(self):
eval_config = eval_pb2.EvalConfig()
person_keypoints_metric = eval_config.parameterized_metric.add()
person_keypoints_metric.coco_keypoint_metrics.class_label = 'person'
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'left_eye'] = 0.1
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'right_eye'] = 0.2
dog_keypoints_metric = eval_config.parameterized_metric.add()
dog_keypoints_metric.coco_keypoint_metrics.class_label = 'dog'
dog_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'tail_start'] = 0.3
dog_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'mouth'] = 0.4
categories = self._get_categories_list_with_keypoints()
evaluator = eval_util.get_evaluators(
eval_config, categories, evaluator_options=None)
# Verify keypoint evaluator class variables.
self.assertLen(evaluator, 3)
self.assertFalse(evaluator[0]._include_metrics_per_category)
self.assertEqual(evaluator[1]._category_name, 'person')
self.assertEqual(evaluator[2]._category_name, 'dog')
self.assertAllEqual(evaluator[1]._keypoint_ids, [0, 3])
self.assertAllEqual(evaluator[2]._keypoint_ids, [1, 2])
self.assertAllClose([0.1, 0.2], evaluator[1]._oks_sigmas)
self.assertAllClose([0.3, 0.4], evaluator[2]._oks_sigmas)
def test_get_evaluator_with_unmatched_label(self):
eval_config = eval_pb2.EvalConfig()
person_keypoints_metric = eval_config.parameterized_metric.add()
person_keypoints_metric.coco_keypoint_metrics.class_label = 'unmatched'
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'kpt'] = 0.1
categories = self._get_categories_list_with_keypoints()
evaluator = eval_util.get_evaluators(
eval_config, categories, evaluator_options=None)
self.assertLen(evaluator, 1)
self.assertNotIsInstance(
evaluator[0], coco_evaluation.CocoKeypointEvaluator)
def test_padded_image_result_dict(self):
input_data_fields = fields.InputDataFields
detection_fields = fields.DetectionResultFields
key = tf.constant([str(i) for i in range(2)])
detection_boxes = np.array([[[0., 0., 1., 1.]], [[0.0, 0.0, 0.5, 0.5]]],
dtype=np.float32)
detection_keypoints = np.array([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]],
dtype=np.float32)
def graph_fn():
detections = {
detection_fields.detection_boxes:
tf.constant(detection_boxes),
detection_fields.detection_scores:
tf.constant([[1.], [1.]]),
detection_fields.detection_classes:
tf.constant([[1], [2]]),
detection_fields.num_detections:
tf.constant([1, 1]),
detection_fields.detection_keypoints:
tf.tile(
tf.reshape(
tf.constant(detection_keypoints), shape=[1, 1, 3, 2]),
multiples=[2, 1, 1, 1])
}
gt_boxes = detection_boxes
groundtruth = {
input_data_fields.groundtruth_boxes:
tf.constant(gt_boxes),
input_data_fields.groundtruth_classes:
tf.constant([[1.], [1.]]),
input_data_fields.groundtruth_keypoints:
tf.tile(
tf.reshape(
tf.constant(detection_keypoints), shape=[1, 1, 3, 2]),
multiples=[2, 1, 1, 1])
}
image = tf.zeros((2, 100, 100, 3), dtype=tf.float32)
true_image_shapes = tf.constant([[100, 100, 3], [50, 100, 3]])
original_image_spatial_shapes = tf.constant([[200, 200], [150, 300]])
result = eval_util.result_dict_for_batched_example(
image, key, detections, groundtruth,
scale_to_absolute=True,
true_image_shapes=true_image_shapes,
original_image_spatial_shapes=original_image_spatial_shapes,
max_gt_boxes=tf.constant(1))
return (result[input_data_fields.groundtruth_boxes],
result[input_data_fields.groundtruth_keypoints],
result[detection_fields.detection_boxes],
result[detection_fields.detection_keypoints])
(gt_boxes, gt_keypoints, detection_boxes,
detection_keypoints) = self.execute_cpu(graph_fn, [])
self.assertAllEqual(
[[[0., 0., 200., 200.]], [[0.0, 0.0, 150., 150.]]],
gt_boxes)
self.assertAllClose([[[[0., 0.], [100., 100.], [200., 200.]]],
[[[0., 0.], [150., 150.], [300., 300.]]]],
gt_keypoints)
# Predictions from the model are not scaled.
self.assertAllEqual(
[[[0., 0., 200., 200.]], [[0.0, 0.0, 75., 150.]]],
detection_boxes)
self.assertAllClose([[[[0., 0.], [100., 100.], [200., 200.]]],
[[[0., 0.], [75., 150.], [150., 300.]]]],
detection_keypoints)
def test_evaluator_options_from_eval_config_no_super_categories(self):
eval_config_text_proto = """
metrics_set: "coco_detection_metrics"
metrics_set: "coco_mask_metrics"
include_metrics_per_category: true
use_moving_averages: false
batch_size: 1;
"""
eval_config = eval_pb2.EvalConfig()
text_format.Merge(eval_config_text_proto, eval_config)
evaluator_options = eval_util.evaluator_options_from_eval_config(
eval_config)
self.assertNotIn('super_categories', evaluator_options['coco_mask_metrics'])
def test_evaluator_options_from_eval_config_with_super_categories(self):
eval_config_text_proto = """
metrics_set: "coco_detection_metrics"
metrics_set: "coco_mask_metrics"
include_metrics_per_category: true
use_moving_averages: false
batch_size: 1;
super_categories {
key: "supercat1"
value: "a,b,c"
}
super_categories {
key: "supercat2"
value: "d,e,f"
}
"""
eval_config = eval_pb2.EvalConfig()
text_format.Merge(eval_config_text_proto, eval_config)
evaluator_options = eval_util.evaluator_options_from_eval_config(
eval_config)
self.assertIn('super_categories', evaluator_options['coco_mask_metrics'])
super_categories = evaluator_options[
'coco_mask_metrics']['super_categories']
self.assertIn('supercat1', super_categories)
self.assertIn('supercat2', super_categories)
self.assertAllEqual(super_categories['supercat1'], ['a', 'b', 'c'])
self.assertAllEqual(super_categories['supercat2'], ['d', 'e', 'f'])
if __name__ == '__main__':
tf.test.main()