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import unittest |
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import torch |
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from mmengine import DefaultScope |
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from mmdet3d.registry import MODELS |
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from mmdet3d.testing import (create_detector_inputs, get_detector_cfg, |
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setup_seed) |
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class TestH3D(unittest.TestCase): |
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def test_h3dnet(self): |
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import mmdet3d.models |
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assert hasattr(mmdet3d.models, 'H3DNet') |
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DefaultScope.get_instance('test_H3DNet', scope_name='mmdet3d') |
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setup_seed(0) |
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voxel_net_cfg = get_detector_cfg('h3dnet/h3dnet_8xb3_scannet-seg.py') |
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model = MODELS.build(voxel_net_cfg) |
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num_gt_instance = 5 |
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packed_inputs = create_detector_inputs( |
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num_gt_instance=num_gt_instance, |
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points_feat_dim=4, |
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bboxes_3d_type='depth', |
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with_pts_semantic_mask=True, |
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with_pts_instance_mask=True) |
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if torch.cuda.is_available(): |
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model = model.cuda() |
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with torch.no_grad(): |
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data = model.data_preprocessor(packed_inputs, True) |
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results = model.forward(**data, mode='predict') |
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self.assertEqual(len(results), 1) |
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self.assertIn('bboxes_3d', results[0].pred_instances_3d) |
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self.assertIn('scores_3d', results[0].pred_instances_3d) |
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self.assertIn('labels_3d', results[0].pred_instances_3d) |
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with torch.no_grad(): |
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losses = model.forward(**data, mode='loss') |
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self.assertGreater(losses['vote_loss'], 0) |
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self.assertGreater(losses['objectness_loss'], 0) |
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self.assertGreater(losses['center_loss'], 0) |
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