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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
import mmcv
import mmengine
import numpy as np
from mmengine.utils import is_list_of
from parameterized import parameterized
from mmdet3d.apis import MonoDet3DInferencer
from mmdet3d.structures import Det3DDataSample
class TestMonoDet3DInferencer(TestCase):
def test_init(self):
# init from metafile
MonoDet3DInferencer('pgd_kitti')
# init from cfg
MonoDet3DInferencer(
'configs/pgd/pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d.py',
'https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/'
'pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d/'
'pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d_'
'20211022_102608-8a97533b.pth')
def assert_predictions_equal(self, preds1, preds2):
for pred1, pred2 in zip(preds1, preds2):
if 'bboxes_3d' in pred1:
self.assertTrue(
np.allclose(pred1['bboxes_3d'], pred2['bboxes_3d'], 0.1))
if 'scores_3d' in pred1:
self.assertTrue(
np.allclose(pred1['scores_3d'], pred2['scores_3d'], 0.1))
if 'labels_3d' in pred1:
self.assertTrue(
np.allclose(pred1['labels_3d'], pred2['labels_3d']))
@parameterized.expand(['pgd_kitti'])
def test_call(self, model):
# single img
img_path = 'demo/data/kitti/000008.png'
infos_path = 'demo/data/kitti/000008.pkl'
inferencer = MonoDet3DInferencer(model)
inputs = dict(img=img_path, infos=infos_path)
res_path = inferencer(inputs, return_vis=True)
# ndarray
img = mmcv.imread(img_path)
inputs = dict(img=img, infos=infos_path)
res_ndarray = inferencer(inputs, return_vis=True)
self.assert_predictions_equal(res_path['predictions'],
res_ndarray['predictions'])
self.assertIn('visualization', res_path)
self.assertIn('visualization', res_ndarray)
# multiple images
inputs = [
dict(
img='demo/data/kitti/000008.png',
infos='demo/data/kitti/000008.pkl'),
dict(
img='demo/data/kitti/000008.png',
infos='demo/data/kitti/000008.pkl')
]
res_path = inferencer(inputs, return_vis=True)
# list of ndarray
imgs = [mmcv.imread(p['img']) for p in inputs]
inputs = [
dict(img=imgs[0], infos='demo/data/kitti/000008.pkl'),
dict(img=imgs[1], infos='demo/data/kitti/000008.pkl')
]
res_ndarray = inferencer(inputs, return_vis=True)
self.assert_predictions_equal(res_path['predictions'],
res_ndarray['predictions'])
self.assertIn('visualization', res_path)
self.assertIn('visualization', res_ndarray)
@parameterized.expand(['pgd_kitti'])
def test_visualize(self, model):
inputs = dict(
img='demo/data/kitti/000008.png',
infos='demo/data/kitti/000008.pkl')
inferencer = MonoDet3DInferencer(model)
# img_out_dir
with tempfile.TemporaryDirectory() as tmp_dir:
inferencer(inputs, out_dir=tmp_dir)
self.assertTrue(
osp.exists(osp.join(tmp_dir, 'vis_camera/CAM2/000008.png')))
@parameterized.expand(['pgd_kitti'])
def test_postprocess(self, model):
# return_datasample
img_path = 'demo/data/kitti/000008.png'
infos_path = 'demo/data/kitti/000008.pkl'
inputs = dict(img=img_path, infos=infos_path)
inferencer = MonoDet3DInferencer(model)
res = inferencer(inputs, return_datasamples=True)
self.assertTrue(is_list_of(res['predictions'], Det3DDataSample))
# pred_out_dir
with tempfile.TemporaryDirectory() as tmp_dir:
inputs = dict(img=img_path, infos=infos_path)
res = inferencer(inputs, print_result=True, out_dir=tmp_dir)
dumped_res = mmengine.load(
osp.join(tmp_dir, 'preds', '000008.json'))
self.assertEqual(res['predictions'][0], dumped_res)
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