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# Copyright (c) OpenMMLab. All rights reserved.
# adapted from https://github.com/ScanNet/ScanNet/blob/master/BenchmarkScripts/3d_evaluation/evaluate_semantic_instance.py # noqa
from copy import deepcopy
import numpy as np
from . import util_3d
def evaluate_matches(matches, class_labels, options):
"""Evaluate instance segmentation from matched gt and predicted instances
for all scenes.
Args:
matches (dict): Contains gt2pred and pred2gt infos for every scene.
class_labels (tuple[str]): Class names.
options (dict): ScanNet evaluator options. See get_options.
Returns:
np.array: Average precision scores for all thresholds and categories.
"""
overlaps = options['overlaps']
min_region_sizes = [options['min_region_sizes'][0]]
dist_threshes = [options['distance_threshes'][0]]
dist_confs = [options['distance_confs'][0]]
# results: class x overlap
ap = np.zeros((len(dist_threshes), len(class_labels), len(overlaps)))
for di, (min_region_size, distance_thresh, distance_conf) in enumerate(
zip(min_region_sizes, dist_threshes, dist_confs)):
for oi, overlap_th in enumerate(overlaps):
pred_visited = {}
for m in matches:
for label_name in class_labels:
for p in matches[m]['pred'][label_name]:
if 'filename' in p:
pred_visited[p['filename']] = False
for li, label_name in enumerate(class_labels):
y_true = np.empty(0)
y_score = np.empty(0)
hard_false_negatives = 0
has_gt = False
has_pred = False
for m in matches:
pred_instances = matches[m]['pred'][label_name]
gt_instances = matches[m]['gt'][label_name]
# filter groups in ground truth
gt_instances = [
gt for gt in gt_instances
if gt['instance_id'] >= 1000 and gt['vert_count'] >=
min_region_size and gt['med_dist'] <= distance_thresh
and gt['dist_conf'] >= distance_conf
]
if gt_instances:
has_gt = True
if pred_instances:
has_pred = True
cur_true = np.ones(len(gt_instances))
cur_score = np.ones(len(gt_instances)) * (-float('inf'))
cur_match = np.zeros(len(gt_instances), dtype=bool)
# collect matches
for (gti, gt) in enumerate(gt_instances):
found_match = False
for pred in gt['matched_pred']:
# greedy assignments
if pred_visited[pred['filename']]:
continue
overlap = float(pred['intersection']) / (
gt['vert_count'] + pred['vert_count'] -
pred['intersection'])
if overlap > overlap_th:
confidence = pred['confidence']
# if already have a prediction for this gt,
# the prediction with the lower score is automatically a false positive # noqa
if cur_match[gti]:
max_score = max(cur_score[gti], confidence)
min_score = min(cur_score[gti], confidence)
cur_score[gti] = max_score
# append false positive
cur_true = np.append(cur_true, 0)
cur_score = np.append(cur_score, min_score)
cur_match = np.append(cur_match, True)
# otherwise set score
else:
found_match = True
cur_match[gti] = True
cur_score[gti] = confidence
pred_visited[pred['filename']] = True
if not found_match:
hard_false_negatives += 1
# remove non-matched ground truth instances
cur_true = cur_true[cur_match]
cur_score = cur_score[cur_match]
# collect non-matched predictions as false positive
for pred in pred_instances:
found_gt = False
for gt in pred['matched_gt']:
overlap = float(gt['intersection']) / (
gt['vert_count'] + pred['vert_count'] -
gt['intersection'])
if overlap > overlap_th:
found_gt = True
break
if not found_gt:
num_ignore = pred['void_intersection']
for gt in pred['matched_gt']:
# group?
if gt['instance_id'] < 1000:
num_ignore += gt['intersection']
# small ground truth instances
if gt['vert_count'] < min_region_size or gt[
'med_dist'] > distance_thresh or gt[
'dist_conf'] < distance_conf:
num_ignore += gt['intersection']
proportion_ignore = float(
num_ignore) / pred['vert_count']
# if not ignored append false positive
if proportion_ignore <= overlap_th:
cur_true = np.append(cur_true, 0)
confidence = pred['confidence']
cur_score = np.append(cur_score, confidence)
# append to overall results
y_true = np.append(y_true, cur_true)
y_score = np.append(y_score, cur_score)
# compute average precision
if has_gt and has_pred:
# compute precision recall curve first
# sorting and cumsum
score_arg_sort = np.argsort(y_score)
y_score_sorted = y_score[score_arg_sort]
y_true_sorted = y_true[score_arg_sort]
y_true_sorted_cumsum = np.cumsum(y_true_sorted)
# unique thresholds
(thresholds, unique_indices) = np.unique(
y_score_sorted, return_index=True)
num_prec_recall = len(unique_indices) + 1
# prepare precision recall
num_examples = len(y_score_sorted)
# follow https://github.com/ScanNet/ScanNet/pull/26 ? # noqa
num_true_examples = y_true_sorted_cumsum[-1] if len(
y_true_sorted_cumsum) > 0 else 0
precision = np.zeros(num_prec_recall)
recall = np.zeros(num_prec_recall)
# deal with the first point
y_true_sorted_cumsum = np.append(y_true_sorted_cumsum, 0)
# deal with remaining
for idx_res, idx_scores in enumerate(unique_indices):
cumsum = y_true_sorted_cumsum[idx_scores - 1]
tp = num_true_examples - cumsum
fp = num_examples - idx_scores - tp
fn = cumsum + hard_false_negatives
p = float(tp) / (tp + fp)
r = float(tp) / (tp + fn)
precision[idx_res] = p
recall[idx_res] = r
# first point in curve is artificial
precision[-1] = 1.
recall[-1] = 0.
# compute average of precision-recall curve
recall_for_conv = np.copy(recall)
recall_for_conv = np.append(recall_for_conv[0],
recall_for_conv)
recall_for_conv = np.append(recall_for_conv, 0.)
stepWidths = np.convolve(recall_for_conv, [-0.5, 0, 0.5],
'valid')
# integrate is now simply a dot product
ap_current = np.dot(precision, stepWidths)
elif has_gt:
ap_current = 0.0
else:
ap_current = float('nan')
ap[di, li, oi] = ap_current
return ap
def compute_averages(aps, options, class_labels):
"""Averages AP scores for all categories.
Args:
aps (np.array): AP scores for all thresholds and categories.
options (dict): ScanNet evaluator options. See get_options.
class_labels (tuple[str]): Class names.
Returns:
dict: Overall and per-category AP scores.
"""
d_inf = 0
o50 = np.where(np.isclose(options['overlaps'], 0.5))
o25 = np.where(np.isclose(options['overlaps'], 0.25))
o_all_but25 = np.where(
np.logical_not(np.isclose(options['overlaps'], 0.25)))
avg_dict = {}
avg_dict['all_ap'] = np.nanmean(aps[d_inf, :, o_all_but25])
avg_dict['all_ap_50%'] = np.nanmean(aps[d_inf, :, o50])
avg_dict['all_ap_25%'] = np.nanmean(aps[d_inf, :, o25])
avg_dict['classes'] = {}
for (li, label_name) in enumerate(class_labels):
avg_dict['classes'][label_name] = {}
avg_dict['classes'][label_name]['ap'] = np.average(aps[d_inf, li,
o_all_but25])
avg_dict['classes'][label_name]['ap50%'] = np.average(aps[d_inf, li,
o50])
avg_dict['classes'][label_name]['ap25%'] = np.average(aps[d_inf, li,
o25])
return avg_dict
def assign_instances_for_scan(pred_info, gt_ids, options, valid_class_ids,
class_labels, id_to_label):
"""Assign gt and predicted instances for a single scene.
Args:
pred_info (dict): Predicted masks, labels and scores.
gt_ids (np.array): Ground truth instance masks.
options (dict): ScanNet evaluator options. See get_options.
valid_class_ids (tuple[int]): Ids of valid categories.
class_labels (tuple[str]): Class names.
id_to_label (dict[int, str]): Mapping of valid class id to class label.
Returns:
dict: Per class assigned gt to predicted instances.
dict: Per class assigned predicted to gt instances.
"""
# get gt instances
gt_instances = util_3d.get_instances(gt_ids, valid_class_ids, class_labels,
id_to_label)
# associate
gt2pred = deepcopy(gt_instances)
for label in gt2pred:
for gt in gt2pred[label]:
gt['matched_pred'] = []
pred2gt = {}
for label in class_labels:
pred2gt[label] = []
num_pred_instances = 0
# mask of void labels in the ground truth
bool_void = np.logical_not(np.in1d(gt_ids // 1000, valid_class_ids))
# go through all prediction masks
for pred_mask_file in pred_info:
label_id = int(pred_info[pred_mask_file]['label_id'])
conf = pred_info[pred_mask_file]['conf']
if not label_id in id_to_label: # noqa E713
continue
label_name = id_to_label[label_id]
# read the mask
pred_mask = pred_info[pred_mask_file]['mask']
if len(pred_mask) != len(gt_ids):
raise ValueError('len(pred_mask) != len(gt_ids)')
# convert to binary
pred_mask = np.not_equal(pred_mask, 0)
num = np.count_nonzero(pred_mask)
if num < options['min_region_sizes'][0]:
continue # skip if empty
pred_instance = {}
pred_instance['filename'] = pred_mask_file
pred_instance['pred_id'] = num_pred_instances
pred_instance['label_id'] = label_id
pred_instance['vert_count'] = num
pred_instance['confidence'] = conf
pred_instance['void_intersection'] = np.count_nonzero(
np.logical_and(bool_void, pred_mask))
# matched gt instances
matched_gt = []
# go through all gt instances with matching label
for (gt_num, gt_inst) in enumerate(gt2pred[label_name]):
intersection = np.count_nonzero(
np.logical_and(gt_ids == gt_inst['instance_id'], pred_mask))
if intersection > 0:
gt_copy = gt_inst.copy()
pred_copy = pred_instance.copy()
gt_copy['intersection'] = intersection
pred_copy['intersection'] = intersection
matched_gt.append(gt_copy)
gt2pred[label_name][gt_num]['matched_pred'].append(pred_copy)
pred_instance['matched_gt'] = matched_gt
num_pred_instances += 1
pred2gt[label_name].append(pred_instance)
return gt2pred, pred2gt
def scannet_eval(preds, gts, options, valid_class_ids, class_labels,
id_to_label):
"""Evaluate instance segmentation in ScanNet protocol.
Args:
preds (list[dict]): Per scene predictions of mask, label and
confidence.
gts (list[np.array]): Per scene ground truth instance masks.
options (dict): ScanNet evaluator options. See get_options.
valid_class_ids (tuple[int]): Ids of valid categories.
class_labels (tuple[str]): Class names.
id_to_label (dict[int, str]): Mapping of valid class id to class label.
Returns:
dict: Overall and per-category AP scores.
"""
options = get_options(options)
matches = {}
for i, (pred, gt) in enumerate(zip(preds, gts)):
matches_key = i
# assign gt to predictions
gt2pred, pred2gt = assign_instances_for_scan(pred, gt, options,
valid_class_ids,
class_labels, id_to_label)
matches[matches_key] = {}
matches[matches_key]['gt'] = gt2pred
matches[matches_key]['pred'] = pred2gt
ap_scores = evaluate_matches(matches, class_labels, options)
avgs = compute_averages(ap_scores, options, class_labels)
return avgs
def get_options(options=None):
"""Set ScanNet evaluator options.
Args:
options (dict, optional): Not default options. Default: None.
Returns:
dict: Updated options with all 4 keys.
"""
assert options is None or isinstance(options, dict)
_options = dict(
overlaps=np.append(np.arange(0.5, 0.95, 0.05), 0.25),
min_region_sizes=np.array([100]),
distance_threshes=np.array([float('inf')]),
distance_confs=np.array([-float('inf')]))
if options is not None:
_options.update(options)
return _options
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