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from unittest import TestCase |
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import torch |
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from mmengine import ConfigDict, DefaultScope |
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from mmdet3d.models import Seg3DTTAModel |
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from mmdet3d.registry import MODELS |
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from mmdet3d.structures import Det3DDataSample |
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from mmdet3d.testing import get_detector_cfg |
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class TestSeg3DTTAModel(TestCase): |
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def test_seg3d_tta_model(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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segmentor3d_cfg = get_detector_cfg( |
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'cylinder3d/cylinder3d_4xb4-3x_semantickitti.py') |
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cfg = ConfigDict(type='Seg3DTTAModel', module=segmentor3d_cfg) |
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model: Seg3DTTAModel = MODELS.build(cfg) |
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points = [] |
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data_samples = [] |
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pcd_horizontal_flip_list = [False, False, True, True] |
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pcd_vertical_flip_list = [False, True, False, True] |
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for i in range(4): |
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points.append({'points': [torch.randn(200, 4)]}) |
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data_samples.append([ |
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Det3DDataSample( |
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metainfo=dict( |
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pcd_horizontal_flip=pcd_horizontal_flip_list[i], |
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pcd_vertical_flip=pcd_vertical_flip_list[i])) |
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]) |
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if torch.cuda.is_available(): |
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model.eval().cuda() |
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model.test_step(dict(inputs=points, data_samples=data_samples)) |
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