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EasyCV/tests/predictors/test_bevformer_predictor.py

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# Copyright (c) Alibaba, Inc. and its affiliates.
import os
import unittest
import numpy as np
import torch
from numpy.testing import assert_array_almost_equal
from tests.ut_config import (PRETRAINED_MODEL_BEVFORMER_BASE,
SMALL_NUSCENES_PATH)
import easycv
from easycv.predictors import BEVFormerPredictor
class BEVFormerPredictorTest(unittest.TestCase):
def setUp(self):
print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
def _assert_results(self, results, assert_value=True):
res = results['pts_bbox']
self.assertEqual(res['scores_3d'].shape, torch.Size([300]))
self.assertEqual(res['labels_3d'].shape, torch.Size([300]))
self.assertEqual(res['boxes_3d'].bev.shape, torch.Size([300, 5]))
self.assertEqual(res['boxes_3d'].bottom_center.shape,
torch.Size([300, 3]))
self.assertEqual(res['boxes_3d'].bottom_height.shape,
torch.Size([300]))
self.assertEqual(res['boxes_3d'].center.shape, torch.Size([300, 3]))
self.assertEqual(res['boxes_3d'].corners.shape, torch.Size([300, 8,
3]))
self.assertEqual(res['boxes_3d'].dims.shape, torch.Size([300, 3]))
self.assertEqual(res['boxes_3d'].gravity_center.shape,
torch.Size([300, 3]))
self.assertEqual(res['boxes_3d'].height.shape, torch.Size([300]))
self.assertEqual(res['boxes_3d'].nearest_bev.shape,
torch.Size([300, 4]))
self.assertEqual(res['boxes_3d'].tensor.shape, torch.Size([300, 9]))
self.assertEqual(res['boxes_3d'].top_height.shape, torch.Size([300]))
self.assertEqual(res['boxes_3d'].volume.shape, torch.Size([300]))
self.assertEqual(res['boxes_3d'].yaw.shape, torch.Size([300]))
if not assert_value:
return
assert_array_almost_equal(
res['scores_3d'][:5].numpy(),
np.array([0.982, 0.982, 0.982, 0.982, 0.981], dtype=np.float32),
decimal=3)
assert_array_almost_equal(res['labels_3d'][:10].numpy(),
np.array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5]))
assert_array_almost_equal(
res['boxes_3d'].bev[:2].numpy(),
np.array([[9.341, -2.664, 2.034, 0.657, 1.819],
[6.945, -18.833, 2.047, 0.661, 1.694]],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].bottom_center[:2].numpy(),
np.array([[9.341, -2.664, -1.849], [6.945, -18.833, -2.295]],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].bottom_height[:5].numpy(),
np.array([-1.849, -2.332, -2.295, -1.508, -1.204],
dtype=np.float32),
decimal=1)
assert_array_almost_equal(
res['boxes_3d'].center[:2].numpy(),
np.array([[9.341, -2.664, -1.849], [6.945, -18.833, -2.295]],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].corners[:1][0][:3].numpy(),
np.array([[9.91, -3.569, -1.849], [9.91, -3.569, -0.742],
[9.273, -3.73, -0.742]],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].dims[:2].numpy(),
np.array([[2.034, 0.657, 1.107], [2.047, 0.661, 1.101]],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].gravity_center[:2].numpy(),
np.array([[9.341, -2.664, -1.295], [6.945, -18.833, -1.745]],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].height[:5].numpy(),
np.array([1.107, 1.101, 1.082, 1.098, 1.073], dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].nearest_bev[:2].numpy(),
np.array([[9.013, -3.681, 9.67, -1.647],
[6.615, -19.857, 7.276, -17.81]],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].tensor[:1].numpy(),
np.array([[
9.340, -2.664, -1.849, 2.0343, 6.568e-01, 1.107, 1.819,
-8.636e-06, 2.034e-05
]],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].top_height[:5].numpy(),
np.array([-0.742, -1.194, -1.25, -0.411, -0.132],
dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].volume[:5].numpy(),
np.array([1.478, 1.49, 1.435, 1.495, 1.47], dtype=np.float32),
decimal=3)
assert_array_almost_equal(
res['boxes_3d'].yaw[:5].numpy(),
np.array([1.819, 1.694, 1.659, 1.62, 1.641], dtype=np.float32),
decimal=3)
def test_single(self):
model_path = PRETRAINED_MODEL_BEVFORMER_BASE
single_ann_file = os.path.join(SMALL_NUSCENES_PATH,
'inference/single_sample.pkl')
easycv_dir = os.path.dirname(easycv.__file__)
if os.path.exists(os.path.join(easycv_dir, 'configs')):
config_dir = os.path.join(easycv_dir, 'configs')
else:
config_dir = os.path.join(os.path.dirname(easycv_dir), 'configs')
config_file = os.path.join(
config_dir,
'detection3d/bevformer/bevformer_base_r101_dcn_nuscenes.py')
predictor = BEVFormerPredictor(
model_path=model_path,
config_file=config_file,
)
results = predictor(single_ann_file)
self.assertEqual(len(results), 1)
for result in results:
self._assert_results(result)
def test_batch(self):
model_path = PRETRAINED_MODEL_BEVFORMER_BASE
single_ann_file = os.path.join(SMALL_NUSCENES_PATH,
'inference/single_sample.pkl')
easycv_dir = os.path.dirname(easycv.__file__)
if os.path.exists(os.path.join(easycv_dir, 'configs')):
config_dir = os.path.join(easycv_dir, 'configs')
else:
config_dir = os.path.join(os.path.dirname(easycv_dir), 'configs')
config_file = os.path.join(
config_dir,
'detection3d/bevformer/bevformer_base_r101_dcn_nuscenes.py')
predictor = BEVFormerPredictor(
model_path=model_path, config_file=config_file, batch_size=2)
results = predictor([single_ann_file, single_ann_file])
self.assertEqual(len(results), 2)
# Input the same sample continuously, the output value is different,
# because the model will record the features of the previous sample to infer the next sample
self._assert_results(results[0])
self._assert_results(results[1], assert_value=False)
if __name__ == '__main__':
unittest.main()
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