giantmonkeyTC
2344
34d1f8b
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 TestPartA2(unittest.TestCase):
def test_parta2(self):
import mmdet3d.models
assert hasattr(mmdet3d.models, 'PartA2')
DefaultScope.get_instance('test_parta2', scope_name='mmdet3d')
setup_seed(0)
parta2_cfg = get_detector_cfg(
'parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py')
model = MODELS.build(parta2_cfg)
num_gt_instance = 2
packed_inputs = create_detector_inputs(num_gt_instance=num_gt_instance)
# TODO: Support aug data test
# aug_packed_inputs = [
# create_detector_inputs(num_gt_instance=num_gt_instance),
# create_detector_inputs(num_gt_instance=num_gt_instance + 1)
# ]
# test_aug_test
# metainfo = {
# 'pcd_scale_factor': 1,
# 'pcd_horizontal_flip': 1,
# 'pcd_vertical_flip': 1,
# 'box_type_3d': LiDARInstance3DBoxes
# }
# for item in aug_packed_inputs:
# for batch_id in len(item['data_samples']):
# item['data_samples'][batch_id].set_metainfo(metainfo)
if torch.cuda.is_available():
model = model.cuda()
# test simple_test
with torch.no_grad():
data = model.data_preprocessor(packed_inputs, True)
torch.cuda.empty_cache()
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)
# save the memory
with torch.no_grad():
losses = model.forward(**data, mode='loss')
torch.cuda.empty_cache()
self.assertGreater(losses['loss_rpn_cls'][0], 0)
self.assertGreaterEqual(losses['loss_rpn_bbox'][0], 0)
self.assertGreater(losses['loss_seg'], 0)
self.assertGreater(losses['loss_part'], 0)
self.assertGreater(losses['loss_cls'], 0)