Add apis unittest (#20)

* Finish test_inference

* Finish test_single_gpu_test

* inference supports cpu mode

* Change inference.py

* Add comments

mmdet3d/apis/inference.py

@@ -76,8 +76,9 @@ def inference_detector(model, pcd):
         # scatter to specified GPU
         data = scatter(data, [device.index])[0]
     else:
-        raise NotImplementedError('Not support cpu-only currently')
-
+        # this is a workaround to avoid the bug of MMDataParallel
+        data['img_metas'] = data['img_metas'][0].data
+        data['points'] = data['points'][0].data
     # forward the model
     with torch.no_grad():
         result = model(return_loss=False, rescale=True, **data)

tests/test_apis.py

+import pytest
+import torch
+from mmcv.parallel import MMDataParallel
+from os.path import dirname, exists, join
+
+from mmdet3d.apis import inference_detector, init_detector, single_gpu_test
+from mmdet3d.datasets import build_dataloader, build_dataset
+from mmdet3d.models import build_detector
+
+
+def _get_config_directory():
+    """Find the predefined detector config directory."""
+    try:
+        # Assume we are running in the source mmdetection repo
+        repo_dpath = dirname(dirname(__file__))
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmdet
+        repo_dpath = dirname(dirname(mmdet.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def _get_config_module(fname):
+    """Load a configuration as a python module."""
+    from mmcv import Config
+    config_dpath = _get_config_directory()
+    config_fpath = join(config_dpath, fname)
+    config_mod = Config.fromfile(config_fpath)
+    return config_mod
+
+
+def test_inference_detector():
+    pcd = 'tests/data/kitti/training/velodyne_reduced/000000.bin'
+    detector_cfg = 'configs/pointpillars/hv_pointpillars_secfpn_' \
+                   '6x8_160e_kitti-3d-3class.py'
+    detector = init_detector(detector_cfg, device='cpu')
+    results = inference_detector(detector, pcd)
+    bboxes_3d = results[0]['boxes_3d']
+    scores_3d = results[0]['scores_3d']
+    labels_3d = results[0]['labels_3d']
+    assert bboxes_3d.tensor.shape[0] >= 0
+    assert bboxes_3d.tensor.shape[1] == 7
+    assert scores_3d.shape[0] >= 0
+    assert labels_3d.shape[0] >= 0
+
+
+def test_single_gpu_test():
+    if not torch.cuda.is_available():
+        pytest.skip('test requires GPU and torch+cuda')
+    cfg = _get_config_module('votenet/votenet_16x8_sunrgbd-3d-10class.py')
+    model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
+    dataset_cfg = cfg.data.test
+    dataset_cfg.data_root = './tests/data/sunrgbd'
+    dataset_cfg.ann_file = 'tests/data/sunrgbd/sunrgbd_infos.pkl'
+    dataset = build_dataset(dataset_cfg)
+    data_loader = build_dataloader(
+        dataset,
+        samples_per_gpu=1,
+        workers_per_gpu=cfg.data.workers_per_gpu,
+        dist=False,
+        shuffle=False)
+    model = MMDataParallel(model, device_ids=[0])
+    results = single_gpu_test(model, data_loader)
+    bboxes_3d = results[0]['boxes_3d']
+    scores_3d = results[0]['scores_3d']
+    labels_3d = results[0]['labels_3d']
+    assert bboxes_3d.tensor.shape[0] >= 0
+    assert bboxes_3d.tensor.shape[1] == 7
+    assert scores_3d.shape[0] >= 0
+    assert labels_3d.shape[0] >= 0

tests/test_detectors.py

@@ -65,10 +65,9 @@ def _get_detector_cfg(fname):
     train_cfg = mmcv.Config(copy.deepcopy(config.train_cfg))
     test_cfg = mmcv.Config(copy.deepcopy(config.test_cfg))
 
-    vote_net = model
-    vote_net.update(train_cfg=train_cfg)
-    vote_net.update(test_cfg=test_cfg)
-    return vote_net
+    model.update(train_cfg=train_cfg)
+    model.update(test_cfg=test_cfg)
+    return model
 
 
 def test_get_dynamic_voxelnet():