feat: add unit test

Signed-off-by: Kaan Çolak <kaancolak95@gmail.com>

Author: kaancolak

projects/AutowareCenterPoint/tests/test_pillar_encorder_autoware.py

@@ -0,0 +1,38 @@
+import pytest
+import torch
+import logging
+
+from mmdet3d.registry import MODELS
+
+import projects.AutowareCenterPoint.centerpoint.pillar_encoder_autoware
+def test_pillar_feature_net_autoware():
+
+    use_voxel_center_z = False
+    if not torch.cuda.is_available():
+        pytest.skip('test requires GPU and torch+cuda')
+    pillar_feature_net_autoware_cfg = dict(
+        type='PillarFeatureNetAutoware',
+        in_channels=4,
+        feat_channels=[64],
+        voxel_size=(0.2, 0.2, 8),
+        point_cloud_range=(-51.2, -51.2, -5.0, 51.2, 51.2, 3.0),
+        norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
+        use_voxel_center_z=use_voxel_center_z,
+        with_distance=False,
+    )
+    pillar_feature_net_autoware = MODELS.build(pillar_feature_net_autoware_cfg)
+
+    features = torch.rand([97297, 20, 4])
+    num_voxels = torch.randint(1, 100, [97297])
+    coors = torch.randint(0, 100, [97297, 4])
+
+    features = pillar_feature_net_autoware(features, num_voxels, coors)
+
+    if not use_voxel_center_z:
+        assert pillar_feature_net_autoware.pfn_layers[0].linear.in_features == 9
+    else:
+        assert pillar_feature_net_autoware.pfn_layers[0].linear.in_features == 9
+
+    assert pillar_feature_net_autoware.pfn_layers[0].linear.out_features == 64
+
+    assert features.shape == torch.Size([97297, 64])

projects/AutowareCenterPoint/tests/test_t4_dataset.py

@@ -0,0 +1,80 @@
+import numpy as np
+from mmcv.transforms.base import BaseTransform
+from mmengine.registry import TRANSFORMS
+from mmengine.structures import InstanceData
+
+from mmdet3d.datasets import NuScenesDataset
+from mmdet3d.structures import Det3DDataSample, LiDARInstance3DBoxes
+
+from projects.AutowareCenterPoint.datasets.tier4_dataset import T4Dataset
+
+def _generate_t4_dataset_config():
+    data_root = 'data/sample_dataset/'
+    ann_file = 'T4Dataset_infos_train.pkl'
+    classes = [
+        'car', 'truck', 'bus', 'bicycle', 'pedestrian'
+    ]
+
+    if 'Identity' not in TRANSFORMS:
+
+        @TRANSFORMS.register_module()
+        class Identity(BaseTransform):
+
+            def transform(self, info):
+                packed_input = dict(data_samples=Det3DDataSample())
+                if 'ann_info' in info:
+                    packed_input[
+                        'data_samples'].gt_instances_3d = InstanceData()
+                    packed_input[
+                        'data_samples'].gt_instances_3d.labels_3d = info[
+                            'ann_info']['gt_labels_3d']
+                return packed_input
+
+    pipeline = [
+        dict(type='Identity'),
+    ]
+    modality = dict(use_lidar=True, use_camera=True)
+    data_prefix = dict(
+        pts='samples/LIDAR_TOP',
+        img='samples/CAM_BACK_LEFT',
+        sweeps='sweeps/LIDAR_TOP')
+    return data_root, ann_file, classes, data_prefix, pipeline, modality
+
+
+def test_getitem():
+    np.random.seed(0)
+    data_root, ann_file, classes, data_prefix, pipeline, modality = \
+        _generate_t4_dataset_config()
+
+    t4_dataset = T4Dataset(
+        data_root=data_root,
+        ann_file=ann_file,
+        data_prefix=data_prefix,
+        pipeline=pipeline,
+        metainfo=dict(classes=classes),
+        modality=modality)
+
+    t4_dataset.prepare_data(0)
+    input_dict = t4_dataset.get_data_info(0)
+    # assert the the path should contains data_prefix and data_root
+    assert data_prefix['pts'] in input_dict['lidar_points']['lidar_path']
+    assert data_root in input_dict['lidar_points']['lidar_path']
+
+    for cam_id, img_info in input_dict['images'].items():
+        if 'img_path' in img_info:
+            assert data_prefix['img'] in img_info['img_path']
+            assert data_root in img_info['img_path']
+
+    ann_info = t4_dataset.parse_ann_info(input_dict)
+
+    # assert the keys in ann_info and the type
+    assert 'gt_labels_3d' in ann_info
+    assert ann_info['gt_labels_3d'].dtype == np.int64
+    assert len(ann_info['gt_labels_3d']) == 70
+
+    assert 'gt_bboxes_3d' in ann_info
+    assert isinstance(ann_info['gt_bboxes_3d'], LiDARInstance3DBoxes)
+
+    assert len(t4_dataset.metainfo['classes']) == 5
+    assert input_dict['token'] == '5f73a4f0dd74434260bf72821b24c8d4'
+    assert input_dict['timestamp'] == 1697190328.324525