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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmengine.logging import print_log
from terminaltables import AsciiTable
from .scannet_utils.evaluate_semantic_instance import scannet_eval
def aggregate_predictions(masks, labels, scores, valid_class_ids):
"""Maps predictions to ScanNet evaluator format.
Args:
masks (list[torch.Tensor]): Per scene predicted instance masks.
labels (list[torch.Tensor]): Per scene predicted instance labels.
scores (list[torch.Tensor]): Per scene predicted instance scores.
valid_class_ids (tuple[int]): Ids of valid categories.
Returns:
list[dict]: Per scene aggregated predictions.
"""
infos = []
for id, (mask, label, score) in enumerate(zip(masks, labels, scores)):
mask = mask.clone().numpy()
label = label.clone().numpy()
score = score.clone().numpy()
info = dict()
n_instances = mask.max() + 1
for i in range(n_instances):
# match pred_instance['filename'] from assign_instances_for_scan
file_name = f'{id}_{i}'
info[file_name] = dict()
info[file_name]['mask'] = (mask == i).astype(np.int64)
info[file_name]['label_id'] = valid_class_ids[label[i]]
info[file_name]['conf'] = score[i]
infos.append(info)
return infos
def rename_gt(gt_semantic_masks, gt_instance_masks, valid_class_ids):
"""Maps gt instance and semantic masks to instance masks for ScanNet
evaluator.
Args:
gt_semantic_masks (list[torch.Tensor]): Per scene gt semantic masks.
gt_instance_masks (list[torch.Tensor]): Per scene gt instance masks.
valid_class_ids (tuple[int]): Ids of valid categories.
Returns:
list[np.array]: Per scene instance masks.
"""
renamed_instance_masks = []
for semantic_mask, instance_mask in zip(gt_semantic_masks,
gt_instance_masks):
semantic_mask = semantic_mask.clone().numpy()
instance_mask = instance_mask.clone().numpy()
unique = np.unique(instance_mask)
assert len(unique) < 1000
for i in unique:
semantic_instance = semantic_mask[instance_mask == i]
semantic_unique = np.unique(semantic_instance)
assert len(semantic_unique) == 1
if semantic_unique[0] < len(valid_class_ids):
instance_mask[
instance_mask ==
i] = 1000 * valid_class_ids[semantic_unique[0]] + i
renamed_instance_masks.append(instance_mask)
return renamed_instance_masks
def instance_seg_eval(gt_semantic_masks,
gt_instance_masks,
pred_instance_masks,
pred_instance_labels,
pred_instance_scores,
valid_class_ids,
class_labels,
options=None,
logger=None):
"""Instance Segmentation Evaluation.
Evaluate the result of the instance segmentation.
Args:
gt_semantic_masks (list[torch.Tensor]): Ground truth semantic masks.
gt_instance_masks (list[torch.Tensor]): Ground truth instance masks.
pred_instance_masks (list[torch.Tensor]): Predicted instance masks.
pred_instance_labels (list[torch.Tensor]): Predicted instance labels.
pred_instance_scores (list[torch.Tensor]): Predicted instance labels.
valid_class_ids (tuple[int]): Ids of valid categories.
class_labels (tuple[str]): Names of valid categories.
options (dict, optional): Additional options. Keys may contain:
`overlaps`, `min_region_sizes`, `distance_threshes`,
`distance_confs`. Default: None.
logger (logging.Logger | str, optional): The way to print the mAP
summary. See `mmdet.utils.print_log()` for details. Default: None.
Returns:
dict[str, float]: Dict of results.
"""
assert len(valid_class_ids) == len(class_labels)
id_to_label = {
valid_class_ids[i]: class_labels[i]
for i in range(len(valid_class_ids))
}
preds = aggregate_predictions(
masks=pred_instance_masks,
labels=pred_instance_labels,
scores=pred_instance_scores,
valid_class_ids=valid_class_ids)
gts = rename_gt(gt_semantic_masks, gt_instance_masks, valid_class_ids)
metrics = scannet_eval(
preds=preds,
gts=gts,
options=options,
valid_class_ids=valid_class_ids,
class_labels=class_labels,
id_to_label=id_to_label)
header = ['classes', 'AP_0.25', 'AP_0.50', 'AP']
rows = []
for label, data in metrics['classes'].items():
aps = [data['ap25%'], data['ap50%'], data['ap']]
rows.append([label] + [f'{ap:.4f}' for ap in aps])
aps = metrics['all_ap_25%'], metrics['all_ap_50%'], metrics['all_ap']
footer = ['Overall'] + [f'{ap:.4f}' for ap in aps]
table = AsciiTable([header] + rows + [footer])
table.inner_footing_row_border = True
print_log('\n' + table.table, logger=logger)
return metrics
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