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