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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 TestCylinder3D(unittest.TestCase): |
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def test_cylinder3d(self): |
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import mmdet3d.models |
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assert hasattr(mmdet3d.models, 'Cylinder3D') |
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DefaultScope.get_instance('test_cylinder3d', scope_name='mmdet3d') |
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setup_seed(0) |
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cylinder3d_cfg = get_detector_cfg( |
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'cylinder3d/cylinder3d_4xb4-3x_semantickitti.py') |
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cylinder3d_cfg.decode_head['ignore_index'] = 1 |
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model = MODELS.build(cylinder3d_cfg) |
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num_gt_instance = 3 |
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packed_inputs = create_detector_inputs( |
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num_gt_instance=num_gt_instance, |
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num_classes=1, |
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with_pts_semantic_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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torch.cuda.empty_cache() |
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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('pts_semantic_mask', results[0].pred_pts_seg) |
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losses = model.forward(**data, mode='loss') |
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self.assertGreater(losses['decode.loss_ce'], 0) |
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self.assertGreater(losses['decode.loss_lovasz'], 0) |
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