| |
| from unittest import TestCase |
|
|
| import numpy as np |
| import pytest |
| import torch |
| from mmengine.structures import InstanceData |
|
|
| from mmdet3d.structures import Det3DDataSample, PointData |
|
|
|
|
| def _equal(a, b): |
| if isinstance(a, (torch.Tensor, np.ndarray)): |
| return (a == b).all() |
| else: |
| return a == b |
|
|
|
|
| class TestDet3DDataSample(TestCase): |
|
|
| def test_init(self): |
| meta_info = dict( |
| img_size=[256, 256], |
| scale_factor=np.array([1.5, 1.5]), |
| img_shape=torch.rand(4)) |
|
|
| det3d_data_sample = Det3DDataSample(metainfo=meta_info) |
| assert 'img_size' in det3d_data_sample |
| assert det3d_data_sample.img_size == [256, 256] |
| assert det3d_data_sample.get('img_size') == [256, 256] |
|
|
| def test_setter(self): |
| det3d_data_sample = Det3DDataSample() |
| |
| gt_instances_3d_data = dict( |
| bboxes_3d=torch.rand(4, 7), labels_3d=torch.rand(4)) |
| gt_instances_3d = InstanceData(**gt_instances_3d_data) |
| det3d_data_sample.gt_instances_3d = gt_instances_3d |
| assert 'gt_instances_3d' in det3d_data_sample |
| assert _equal(det3d_data_sample.gt_instances_3d.bboxes_3d, |
| gt_instances_3d_data['bboxes_3d']) |
| assert _equal(det3d_data_sample.gt_instances_3d.labels_3d, |
| gt_instances_3d_data['labels_3d']) |
|
|
| |
| pred_instances_3d_data = dict( |
| bboxes_3d=torch.rand(2, 7), |
| labels_3d=torch.rand(2), |
| scores_3d=torch.rand(2)) |
| pred_instances_3d = InstanceData(**pred_instances_3d_data) |
| det3d_data_sample.pred_instances_3d = pred_instances_3d |
| assert 'pred_instances_3d' in det3d_data_sample |
| assert _equal(det3d_data_sample.pred_instances_3d.bboxes_3d, |
| pred_instances_3d_data['bboxes_3d']) |
| assert _equal(det3d_data_sample.pred_instances_3d.labels_3d, |
| pred_instances_3d_data['labels_3d']) |
| assert _equal(det3d_data_sample.pred_instances_3d.scores_3d, |
| pred_instances_3d_data['scores_3d']) |
|
|
| |
| pts_pred_instances_3d_data = dict( |
| bboxes_3d=torch.rand(2, 7), |
| labels_3d=torch.rand(2), |
| scores_3d=torch.rand(2)) |
| pts_pred_instances_3d = InstanceData(**pts_pred_instances_3d_data) |
| det3d_data_sample.pts_pred_instances_3d = pts_pred_instances_3d |
| assert 'pts_pred_instances_3d' in det3d_data_sample |
| assert _equal(det3d_data_sample.pts_pred_instances_3d.bboxes_3d, |
| pts_pred_instances_3d_data['bboxes_3d']) |
| assert _equal(det3d_data_sample.pts_pred_instances_3d.labels_3d, |
| pts_pred_instances_3d_data['labels_3d']) |
| assert _equal(det3d_data_sample.pts_pred_instances_3d.scores_3d, |
| pts_pred_instances_3d_data['scores_3d']) |
|
|
| |
| img_pred_instances_3d_data = dict( |
| bboxes_3d=torch.rand(2, 7), |
| labels_3d=torch.rand(2), |
| scores_3d=torch.rand(2)) |
| img_pred_instances_3d = InstanceData(**img_pred_instances_3d_data) |
| det3d_data_sample.img_pred_instances_3d = img_pred_instances_3d |
| assert 'img_pred_instances_3d' in det3d_data_sample |
| assert _equal(det3d_data_sample.img_pred_instances_3d.bboxes_3d, |
| img_pred_instances_3d_data['bboxes_3d']) |
| assert _equal(det3d_data_sample.img_pred_instances_3d.labels_3d, |
| img_pred_instances_3d_data['labels_3d']) |
| assert _equal(det3d_data_sample.img_pred_instances_3d.scores_3d, |
| img_pred_instances_3d_data['scores_3d']) |
|
|
| |
| gt_pts_seg_data = dict( |
| pts_instance_mask=torch.rand(20), pts_semantic_mask=torch.rand(20)) |
| gt_pts_seg = PointData(**gt_pts_seg_data) |
| det3d_data_sample.gt_pts_seg = gt_pts_seg |
| assert 'gt_pts_seg' in det3d_data_sample |
| assert _equal(det3d_data_sample.gt_pts_seg.pts_instance_mask, |
| gt_pts_seg_data['pts_instance_mask']) |
| assert _equal(det3d_data_sample.gt_pts_seg.pts_semantic_mask, |
| gt_pts_seg_data['pts_semantic_mask']) |
|
|
| |
| pred_pts_seg_data = dict( |
| pts_instance_mask=torch.rand(20), pts_semantic_mask=torch.rand(20)) |
| pred_pts_seg = PointData(**pred_pts_seg_data) |
| det3d_data_sample.pred_pts_seg = pred_pts_seg |
| assert 'pred_pts_seg' in det3d_data_sample |
| assert _equal(det3d_data_sample.pred_pts_seg.pts_instance_mask, |
| pred_pts_seg_data['pts_instance_mask']) |
| assert _equal(det3d_data_sample.pred_pts_seg.pts_semantic_mask, |
| pred_pts_seg_data['pts_semantic_mask']) |
|
|
| |
| with pytest.raises(AssertionError): |
| det3d_data_sample.pred_instances_3d = torch.rand(2, 4) |
|
|
| with pytest.raises(AssertionError): |
| det3d_data_sample.pred_pts_seg = torch.rand(20) |
|
|
| def test_deleter(self): |
| tmp_instances_3d_data = dict( |
| bboxes_3d=torch.rand(4, 4), labels_3d=torch.rand(4)) |
|
|
| det3d_data_sample = Det3DDataSample() |
| gt_instances_3d = InstanceData(data=tmp_instances_3d_data) |
| det3d_data_sample.gt_instances_3d = gt_instances_3d |
| assert 'gt_instances_3d' in det3d_data_sample |
| del det3d_data_sample.gt_instances_3d |
| assert 'gt_instances_3d' not in det3d_data_sample |
|
|
| pred_instances_3d = InstanceData(data=tmp_instances_3d_data) |
| det3d_data_sample.pred_instances_3d = pred_instances_3d |
| assert 'pred_instances_3d' in det3d_data_sample |
| del det3d_data_sample.pred_instances_3d |
| assert 'pred_instances_3d' not in det3d_data_sample |
|
|
| pts_pred_instances_3d = InstanceData(data=tmp_instances_3d_data) |
| det3d_data_sample.pts_pred_instances_3d = pts_pred_instances_3d |
| assert 'pts_pred_instances_3d' in det3d_data_sample |
| del det3d_data_sample.pts_pred_instances_3d |
| assert 'pts_pred_instances_3d' not in det3d_data_sample |
|
|
| img_pred_instances_3d = InstanceData(data=tmp_instances_3d_data) |
| det3d_data_sample.img_pred_instances_3d = img_pred_instances_3d |
| assert 'img_pred_instances_3d' in det3d_data_sample |
| del det3d_data_sample.img_pred_instances_3d |
| assert 'img_pred_instances_3d' not in det3d_data_sample |
|
|
| pred_pts_seg_data = dict( |
| pts_instance_mask=torch.rand(20), pts_semantic_mask=torch.rand(20)) |
| pred_pts_seg = PointData(**pred_pts_seg_data) |
| det3d_data_sample.pred_pts_seg = pred_pts_seg |
| assert 'pred_pts_seg' in det3d_data_sample |
| del det3d_data_sample.pred_pts_seg |
| assert 'pred_pts_seg' not in det3d_data_sample |
|
|
