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import unittest
import torch
from mmengine import DefaultScope
from mmdet3d.registry import MODELS
from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
setup_seed)
class TestMVXNet(unittest.TestCase):
def test_mvxnet(self):
import mmdet3d.models
assert hasattr(mmdet3d.models, 'DynamicMVXFasterRCNN')
setup_seed(0)
DefaultScope.get_instance('test_mvxnet', scope_name='mmdet3d')
mvx_net_cfg = get_detector_cfg(
'mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py' # noqa
)
model = MODELS.build(mvx_net_cfg)
num_gt_instance = 1
packed_inputs = create_detector_inputs(
with_img=False, num_gt_instance=num_gt_instance, points_feat_dim=4)
if torch.cuda.is_available():
model = model.cuda()
# test simple_test
data = model.data_preprocessor(packed_inputs, True)
# save the memory when do the unitest
with torch.no_grad():
torch.cuda.empty_cache()
losses = model.forward(**data, mode='loss')
assert losses['loss_cls'][0] >= 0
assert losses['loss_bbox'][0] >= 0
assert losses['loss_dir'][0] >= 0
with torch.no_grad():
results = model.forward(**data, mode='predict')
self.assertEqual(len(results), 1)
self.assertIn('bboxes_3d', results[0].pred_instances_3d)
self.assertIn('scores_3d', results[0].pred_instances_3d)
self.assertIn('labels_3d', results[0].pred_instances_3d)
# TODO test_aug_test
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