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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet3d.evaluation import instance_seg_eval
def test_instance_seg_eval():
valid_class_ids = (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
36, 39)
class_labels = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
n_points_list = [3300, 3000]
gt_labels_list = [[0, 0, 0, 0, 0, 0, 14, 14, 2, 1],
[13, 13, 2, 1, 3, 3, 0, 0, 0]]
gt_instance_masks = []
gt_semantic_masks = []
pred_instance_masks = []
pred_instance_labels = []
pred_instance_scores = []
for n_points, gt_labels in zip(n_points_list, gt_labels_list):
gt_instance_mask = np.ones(n_points, dtype=np.int64) * -1
gt_semantic_mask = np.ones(n_points, dtype=np.int64) * -1
pred_instance_mask = np.ones(n_points, dtype=np.int64) * -1
labels = []
scores = []
for i, gt_label in enumerate(gt_labels):
begin = i * 300
end = begin + 300
gt_instance_mask[begin:end] = i
gt_semantic_mask[begin:end] = gt_label
pred_instance_mask[begin:end] = i
labels.append(gt_label)
scores.append(.99)
gt_instance_masks.append(torch.tensor(gt_instance_mask))
gt_semantic_masks.append(torch.tensor(gt_semantic_mask))
pred_instance_masks.append(torch.tensor(pred_instance_mask))
pred_instance_labels.append(torch.tensor(labels))
pred_instance_scores.append(torch.tensor(scores))
ret_value = instance_seg_eval(
gt_semantic_masks=gt_semantic_masks,
gt_instance_masks=gt_instance_masks,
pred_instance_masks=pred_instance_masks,
pred_instance_labels=pred_instance_labels,
pred_instance_scores=pred_instance_scores,
valid_class_ids=valid_class_ids,
class_labels=class_labels)
for label in [
'cabinet', 'bed', 'chair', 'sofa', 'showercurtrain', 'toilet'
]:
metrics = ret_value['classes'][label]
assert metrics['ap'] == 1.0
assert metrics['ap50%'] == 1.0
assert metrics['ap25%'] == 1.0
pred_instance_masks[1][2240:2700] = -1
pred_instance_masks[0][2700:3000] = 8
pred_instance_labels[0][9] = 2
ret_value = instance_seg_eval(
gt_semantic_masks=gt_semantic_masks,
gt_instance_masks=gt_instance_masks,
pred_instance_masks=pred_instance_masks,
pred_instance_labels=pred_instance_labels,
pred_instance_scores=pred_instance_scores,
valid_class_ids=valid_class_ids,
class_labels=class_labels)
assert abs(ret_value['classes']['cabinet']['ap50%'] - 0.72916) < 0.01
assert abs(ret_value['classes']['cabinet']['ap25%'] - 0.88888) < 0.01
assert abs(ret_value['classes']['bed']['ap50%'] - 0.5) < 0.01
assert abs(ret_value['classes']['bed']['ap25%'] - 0.5) < 0.01
assert abs(ret_value['classes']['chair']['ap50%'] - 0.375) < 0.01
assert abs(ret_value['classes']['chair']['ap25%'] - 1.0) < 0.01
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