|
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 TestImvoteNet(unittest.TestCase): |
|
|
|
def test_imvotenet_only_img(self): |
|
import mmdet3d.models |
|
|
|
assert hasattr(mmdet3d.models, 'ImVoteNet') |
|
DefaultScope.get_instance('test_imvotenet_img', scope_name='mmdet3d') |
|
setup_seed(0) |
|
votenet_net_cfg = get_detector_cfg( |
|
'imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py') |
|
model = MODELS.build(votenet_net_cfg) |
|
|
|
packed_inputs = create_detector_inputs( |
|
with_points=False, with_img=True, img_size=128) |
|
|
|
if torch.cuda.is_available(): |
|
model = model.cuda() |
|
|
|
with torch.no_grad(): |
|
data = model.data_preprocessor(packed_inputs, True) |
|
results = model.forward(**data, mode='predict') |
|
self.assertEqual(len(results), 1) |
|
self.assertIn('bboxes', results[0].pred_instances) |
|
self.assertIn('scores', results[0].pred_instances) |
|
self.assertIn('labels', results[0].pred_instances) |
|
|
|
|
|
with torch.no_grad(): |
|
torch.cuda.empty_cache() |
|
losses = model.forward(**data, mode='loss') |
|
|
|
self.assertGreater(sum(losses['loss_rpn_cls']), 0) |
|
|
|
self.assertGreater(losses['loss_cls'], 0) |
|
self.assertGreater(losses['loss_bbox'], 0) |
|
|
|
def test_imvotenet(self): |
|
import mmdet3d.models |
|
|
|
assert hasattr(mmdet3d.models, 'ImVoteNet') |
|
DefaultScope.get_instance('test_imvotenet', scope_name='mmdet3d') |
|
setup_seed(0) |
|
votenet_net_cfg = get_detector_cfg( |
|
'imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py') |
|
model = MODELS.build(votenet_net_cfg) |
|
|
|
packed_inputs = create_detector_inputs( |
|
with_points=True, |
|
with_img=True, |
|
img_size=128, |
|
bboxes_3d_type='depth') |
|
|
|
if torch.cuda.is_available(): |
|
model = model.cuda() |
|
|
|
with torch.no_grad(): |
|
data = model.data_preprocessor(packed_inputs, True) |
|
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) |
|
|
|
|
|
with torch.no_grad(): |
|
losses = model.forward(**data, mode='loss') |
|
|
|
self.assertGreater(losses['vote_loss'], 0) |
|
self.assertGreater(losses['objectness_loss'], 0) |
|
self.assertGreater(losses['semantic_loss'], 0) |
|
|