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Commit b55044d8 authored by Jonathan Huang's avatar Jonathan Huang Committed by TF Object Detection Team
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Add export ability to lvis evaluation class to enable submissions to LVIS server. 

PiperOrigin-RevId: 340366780
parent 33cca6c3
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Showing with 79 additions and 46 deletions
research/object_detection/eval_util.py
View file @ b55044d8
......@@ -1192,6 +1192,11 @@ def evaluator_options_from_eval_config(eval_config):
key = super_category
value = eval_config.super_categories[key].split(',')
evaluator_options[eval_metric_fn_key]['super_categories'][key] = value
if eval_metric_fn_key == 'lvis_mask_metrics' and hasattr(
eval_config, 'export_path'):
evaluator_options[eval_metric_fn_key].update({
'export_path': eval_config.export_path
})
elif eval_metric_fn_key == 'precision_at_recall_detection_metrics':
evaluator_options[eval_metric_fn_key] = {
......
research/object_detection/metrics/coco_evaluation.py
View file @ b55044d8
......@@ -1212,8 +1212,10 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
'groundtruth_classes':
groundtruth_classes[:num_gt_box],
'groundtruth_instance_masks':
groundtruth_instance_masks[:num_gt_box][
:original_image_shape[0], :original_image_shape[1]],
groundtruth_instance_masks[
:num_gt_box,
:original_image_shape[0],
:original_image_shape[1]],
'groundtruth_is_crowd':
groundtruth_is_crowd[:num_gt_box]
})
......@@ -1221,8 +1223,10 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
image_id, {
'detection_scores': detection_scores[:num_det_box],
'detection_classes': detection_classes[:num_det_box],
'detection_masks': detection_masks[:num_det_box][
:original_image_shape[0], :original_image_shape[1]]
'detection_masks': detection_masks[
:num_det_box,
:original_image_shape[0],
:original_image_shape[1]]
})
# Unpack items from the evaluation dictionary.
......
research/object_detection/metrics/lvis_evaluation.py
View file @ b55044d8
......@@ -40,13 +40,18 @@ class LVISMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
"""Class to evaluate LVIS mask metrics."""
def __init__(self,
categories):
categories,
include_metrics_per_category=False,
export_path=None):
"""Constructor.
Args:
categories: A list of dicts, each of which has the following keys -
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name e.g., 'cat', 'dog'.
include_metrics_per_category: Additionally include per-category metrics
(this option is currently unsupported).
export_path: Path to export detections to LVIS compatible JSON format.
"""
super(LVISMaskEvaluator, self).__init__(categories)
self._image_ids_with_detections = set([])
......@@ -57,6 +62,10 @@ class LVISMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
self._image_id_to_mask_shape_map = {}
self._image_id_to_verified_neg_classes = {}
self._image_id_to_not_exhaustive_classes = {}
if include_metrics_per_category:
raise ValueError('include_metrics_per_category not yet supported '
'for LVISMaskEvaluator.')
self._export_path = export_path
def clear(self):
"""Clears the state to prepare for a fresh evaluation."""
......@@ -86,10 +95,14 @@ class LVISMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
InputDataFields.groundtruth_instance_masks: uint8 numpy array of shape
[num_masks, image_height, image_width] containing groundtruth masks.
The elements of the array must be in {0, 1}.
InputDataFields.groundtruth_verified_neg_classes: [num_classes]
float indicator vector with values in {0, 1}.
InputDataFields.groundtruth_not_exhaustive_classes: [num_classes]
float indicator vector with values in {0, 1}.
InputDataFields.groundtruth_verified_neg_classes: [num_classes + 1]
float indicator vector with values in {0, 1}. The length is
num_classes + 1 so as to be compatible with the 1-indexed groundtruth
classes.
InputDataFields.groundtruth_not_exhaustive_classes: [num_classes + 1]
float indicator vector with values in {0, 1}. The length is
num_classes + 1 so as to be compatible with the 1-indexed groundtruth
classes.
InputDataFields.groundtruth_area (optional): float numpy array of
shape [num_boxes] containing the area (in the original absolute
coordinates) of the annotated object.
......@@ -116,9 +129,9 @@ class LVISMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
fields.InputDataFields.groundtruth_verified_neg_classes].shape
not_exhaustive_classes_shape = groundtruth_dict[
fields.InputDataFields.groundtruth_not_exhaustive_classes].shape
if verified_neg_classes_shape != (len(self._category_id_set),):
if verified_neg_classes_shape != (len(self._category_id_set) + 1,):
raise ValueError('Invalid shape for verified_neg_classes_shape.')
if not_exhaustive_classes_shape != (len(self._category_id_set),):
if not_exhaustive_classes_shape != (len(self._category_id_set) + 1,):
raise ValueError('Invalid shape for not_exhaustive_classes_shape.')
self._image_id_to_verified_neg_classes[image_id] = np.flatnonzero(
groundtruth_dict[
......@@ -210,6 +223,9 @@ class LVISMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
Returns:
A dictionary holding
"""
if self._export_path:
tf.logging.info('Dumping detections to json.')
self.dump_detections_to_json_file(self._export_path)
tf.logging.info('Performing evaluation on %d images.',
len(self._image_id_to_mask_shape_map.keys()))
# pylint: disable=g-complex-comprehension
......@@ -217,7 +233,7 @@ class LVISMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
'annotations': self._groundtruth_list,
'images': [
{
'id': image_id,
'id': int(image_id),
'height': shape[1],
'width': shape[2],
'neg_category_ids':
......@@ -287,8 +303,10 @@ class LVISMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
input_data_fields.groundtruth_classes:
groundtruth_classes[:num_gt_box],
input_data_fields.groundtruth_instance_masks:
groundtruth_instance_masks[:num_gt_box][
:original_image_shape[0], :original_image_shape[1]],
groundtruth_instance_masks[
:num_gt_box,
:original_image_shape[0],
:original_image_shape[1]],
input_data_fields.groundtruth_verified_neg_classes:
groundtruth_verified_neg_classes,
input_data_fields.groundtruth_not_exhaustive_classes:
......@@ -298,8 +316,10 @@ class LVISMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
image_id, {
'detection_scores': detection_scores[:num_det_box],
'detection_classes': detection_classes[:num_det_box],
'detection_masks': detection_masks[:num_det_box][
:original_image_shape[0], :original_image_shape[1]]
'detection_masks': detection_masks[
:num_det_box,
:original_image_shape[0],
:original_image_shape[1]]
})
# Unpack items from the evaluation dictionary.
......
research/object_detection/metrics/lvis_evaluation_test.py
View file @ b55044d8
......@@ -59,19 +59,19 @@ class LvisMaskEvaluationTest(tf.test.TestCase):
lvis_evaluator = lvis_evaluation.LVISMaskEvaluator(
_get_categories_list())
lvis_evaluator.add_single_ground_truth_image_info(
image_id='image1',
image_id=1,
groundtruth_dict={
fields.InputDataFields.groundtruth_boxes:
np.array([[100., 100., 200., 200.]]),
fields.InputDataFields.groundtruth_classes: np.array([1]),
fields.InputDataFields.groundtruth_instance_masks: masks1,
fields.InputDataFields.groundtruth_verified_neg_classes:
np.array([0, 0, 0]),
np.array([0, 0, 0, 0]),
fields.InputDataFields.groundtruth_not_exhaustive_classes:
np.array([0, 0, 0])
np.array([0, 0, 0, 0])
})
lvis_evaluator.add_single_detected_image_info(
image_id='image1',
image_id=1,
detections_dict={
fields.DetectionResultFields.detection_masks: masks1,
fields.DetectionResultFields.detection_scores:
......@@ -80,19 +80,19 @@ class LvisMaskEvaluationTest(tf.test.TestCase):
np.array([1])
})
lvis_evaluator.add_single_ground_truth_image_info(
image_id='image2',
image_id=2,
groundtruth_dict={
fields.InputDataFields.groundtruth_boxes:
np.array([[50., 50., 100., 100.]]),
fields.InputDataFields.groundtruth_classes: np.array([1]),
fields.InputDataFields.groundtruth_instance_masks: masks2,
fields.InputDataFields.groundtruth_verified_neg_classes:
np.array([0, 0, 0]),
np.array([0, 0, 0, 0]),
fields.InputDataFields.groundtruth_not_exhaustive_classes:
np.array([0, 0, 0])
np.array([0, 0, 0, 0])
})
lvis_evaluator.add_single_detected_image_info(
image_id='image2',
image_id=2,
detections_dict={
fields.DetectionResultFields.detection_masks: masks2,
fields.DetectionResultFields.detection_scores:
......@@ -101,19 +101,19 @@ class LvisMaskEvaluationTest(tf.test.TestCase):
np.array([1])
})
lvis_evaluator.add_single_ground_truth_image_info(
image_id='image3',
image_id=3,
groundtruth_dict={
fields.InputDataFields.groundtruth_boxes:
np.array([[25., 25., 50., 50.]]),
fields.InputDataFields.groundtruth_classes: np.array([1]),
fields.InputDataFields.groundtruth_instance_masks: masks3,
fields.InputDataFields.groundtruth_verified_neg_classes:
np.array([0, 0, 0]),
np.array([0, 0, 0, 0]),
fields.InputDataFields.groundtruth_not_exhaustive_classes:
np.array([0, 0, 0])
np.array([0, 0, 0, 0])
})
lvis_evaluator.add_single_detected_image_info(
image_id='image3',
image_id=3,
detections_dict={
fields.DetectionResultFields.detection_masks: masks3,
fields.DetectionResultFields.detection_scores:
......@@ -130,7 +130,7 @@ class LVISMaskEvaluationPyFuncTest(tf.test.TestCase):
def testAddEvalDict(self):
lvis_evaluator = lvis_evaluation.LVISMaskEvaluator(_get_categories_list())
image_id = tf.constant('image1', dtype=tf.string)
image_id = tf.constant(1, dtype=tf.int32)
groundtruth_boxes = tf.constant(
np.array([[100., 100., 200., 200.], [50., 50., 100., 100.]]),
dtype=tf.float32)
......@@ -143,9 +143,9 @@ class LVISMaskEvaluationPyFuncTest(tf.test.TestCase):
]), dtype=tf.uint8)
original_image_spatial_shapes = tf.constant([[120, 120], [120, 120]],
dtype=tf.int32)
groundtruth_verified_neg_classes = tf.constant(np.array([0, 0, 0]),
groundtruth_verified_neg_classes = tf.constant(np.array([0, 0, 0, 0]),
dtype=tf.float32)
groundtruth_not_exhaustive_classes = tf.constant(np.array([0, 0, 0]),
groundtruth_not_exhaustive_classes = tf.constant(np.array([0, 0, 0, 0]),
dtype=tf.float32)
detection_scores = tf.constant(np.array([.9, .8]), dtype=tf.float32)
detection_classes = tf.constant(np.array([2, 1]), dtype=tf.float32)
......
research/object_detection/metrics/lvis_tools.py
View file @ b55044d8
......@@ -134,7 +134,7 @@ def ExportSingleImageGroundtruthToLVIS(image_id,
groundtruth bounding box.
Args:
image_id: a unique image identifier either of type integer or string.
image_id: a unique image identifier castable to integer.
next_annotation_id: integer specifying the first id to use for the
groundtruth annotations. All annotations are assigned a continuous integer
id starting from this value.
......@@ -187,7 +187,7 @@ def ExportSingleImageGroundtruthToLVIS(image_id,
'id':
next_annotation_id + i,
'image_id':
image_id,
int(image_id),
'category_id':
int(groundtruth_classes[i]),
'bbox':
......@@ -215,7 +215,7 @@ def ExportSingleImageDetectionMasksToLVIS(image_id,
are associated with the same annotation.
Args:
image_id: unique image identifier either of type integer or string.
image_id: unique image identifier castable to integer.
category_id_set: A set of valid class ids. Detections with classes not in
category_id_set are dropped.
detection_masks: uint8 numpy array of shape [num_detections, image_height,
......@@ -251,9 +251,10 @@ def ExportSingleImageDetectionMasksToLVIS(image_id,
for i in range(num_boxes):
if detection_classes[i] in category_id_set:
detections_list.append({
'image_id': image_id,
'image_id': int(image_id),
'category_id': int(detection_classes[i]),
'segmentation': RleCompress(detection_masks[i]),
'score': float(detection_scores[i])
})
return detections_list
research/object_detection/metrics/lvis_tools_test.py
View file @ b55044d8
......@@ -35,7 +35,7 @@ class LVISToolsTest(tf.test.TestCase):
groundtruth_annotations_list = [
{
'id': 1,
'image_id': 'first',
'image_id': 1,
'category_id': 1,
'bbox': [100., 100., 100., 100.],
'area': 100.**2,
......@@ -43,7 +43,7 @@ class LVISToolsTest(tf.test.TestCase):
},
{
'id': 2,
'image_id': 'second',
'image_id': 2,
'category_id': 1,
'bbox': [50., 50., 50., 50.],
'area': 50.**2,
......@@ -52,14 +52,14 @@ class LVISToolsTest(tf.test.TestCase):
]
image_list = [
{
'id': 'first',
'id': 1,
'neg_category_ids': [],
'not_exhaustive_category_ids': [],
'height': 256,
'width': 256
},
{
'id': 'second',
'id': 2,
'neg_category_ids': [],
'not_exhaustive_category_ids': [],
'height': 256,
......@@ -77,13 +77,13 @@ class LVISToolsTest(tf.test.TestCase):
self._detections_list = [
{
'image_id': 'first',
'image_id': 1,
'category_id': 1,
'segmentation': mask1_rle,
'score': .8
},
{
'image_id': 'second',
'image_id': 2,
'category_id': 1,
'segmentation': mask2_rle,
'score': .7
......@@ -106,7 +106,7 @@ class LVISToolsTest(tf.test.TestCase):
classes = np.array([1, 2, 3], dtype=np.int32)
scores = np.array([0.8, 0.2, 0.7], dtype=np.float32)
lvis_annotations = lvis_tools.ExportSingleImageDetectionMasksToLVIS(
image_id='first_image',
image_id=1,
category_id_set=set([1, 2, 3]),
detection_classes=classes,
detection_scores=scores,
......@@ -117,7 +117,7 @@ class LVISToolsTest(tf.test.TestCase):
expected_counts[i])
self.assertTrue(np.all(np.equal(mask.decode(
mask_annotation['segmentation']), masks[i])))
self.assertEqual(mask_annotation['image_id'], 'first_image')
self.assertEqual(mask_annotation['image_id'], 1)
self.assertEqual(mask_annotation['category_id'], classes[i])
self.assertAlmostEqual(mask_annotation['score'], scores[i])
......@@ -137,7 +137,7 @@ class LVISToolsTest(tf.test.TestCase):
expected_counts = ['04', '31', '4']
lvis_annotations = lvis_tools.ExportSingleImageGroundtruthToLVIS(
image_id='first_image',
image_id=1,
category_id_set=set([1, 2, 3]),
next_annotation_id=next_annotation_id,
groundtruth_boxes=boxes,
......@@ -149,7 +149,7 @@ class LVISToolsTest(tf.test.TestCase):
self.assertTrue(np.all(np.equal(mask.decode(
annotation['segmentation']), masks[i])))
self.assertTrue(np.all(np.isclose(annotation['bbox'], lvis_boxes[i])))
self.assertEqual(annotation['image_id'], 'first_image')
self.assertEqual(annotation['image_id'], 1)
self.assertEqual(annotation['category_id'], classes[i])
self.assertEqual(annotation['id'], i + next_annotation_id)
......
research/object_detection/model_lib_v2.py
View file @ b55044d8
......@@ -772,9 +772,12 @@ def eager_eval_loop(
true_image_shapes = None
original_image_spatial_shapes = None
keys = features[inputs.HASH_KEY]
if eval_input_config.include_source_id:
keys = features[fields.InputDataFields.source_id]
eval_dict = eval_util.result_dict_for_batched_example(
eval_images,
features[inputs.HASH_KEY],
keys,
detections,
groundtruth,
class_agnostic=class_agnostic,
......
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