def test_bbox2result():
bboxes = torch.tensor([[0.072, 0.47, 0.84, 0.898],
[0.23, 0.215, 0.781, 0.534],
[0.195, 0.128, 0.643, 0.944],
[0.236, 0.189, 0.689, 0.74],
[0.375, 0.371, 0.726, 0.804],
[0.024, 0.398, 0.776, 0.719]])
labels = torch.tensor([[-1.650, 0.515, 0.798, 1.240],
[1.368, -1.128, 0.037, -1.087],
[0.481, -1.303, 0.501, -0.463],
[-0.356, 0.126, -0.840, 0.438],
[0.079, 1.269, -0.263, -0.538],
[-0.853, 0.391, 0.103, 0.398]])
num_classes = 4
# Test for multi-label
result = bbox2result(bboxes, labels, num_classes)
assert np.all(
np.isclose(
result[0],
np.array([[0.072, 0.47, 0.84, 0.898, 0.515],
[0.236, 0.189, 0.689, 0.74, 0.126],
[0.375, 0.371, 0.726, 0.804, 1.269],
[0.024, 0.398, 0.776, 0.719, 0.391]])))
assert np.all(
np.isclose(
result[1],
np.array([[0.072, 0.47, 0.84, 0.898, 0.798],
[0.23, 0.215, 0.781, 0.534, 0.037],
[0.195, 0.128, 0.643, 0.944, 0.501],
[0.024, 0.398, 0.776, 0.719, 0.103]])))
assert np.all(
np.isclose(
result[2],
np.array([[0.072, 0.47, 0.84, 0.898, 1.24],
[0.236, 0.189, 0.689, 0.74, 0.438],
[0.024, 0.398, 0.776, 0.719, 0.398]])))
# Test for single-label
result = bbox2result(bboxes, labels, num_classes, -1.0)
assert np.all(
np.isclose(result[0], np.array([[0.375, 0.371, 0.726, 0.804, 1.269]])))
assert np.all(
np.isclose(
result[1],
np.array([[0.23, 0.215, 0.781, 0.534, 0.037],
[0.195, 0.128, 0.643, 0.944, 0.501]])))
assert np.all(
np.isclose(
result[2],
np.array([[0.072, 0.47, 0.84, 0.898, 1.240],
[0.236, 0.189, 0.689, 0.74, 0.438],
[0.024, 0.398, 0.776, 0.719, 0.398]])))
def simple_test(self,
x,
proposal_list,
img_metas,
proposals=None,
rescale=False):
"""Defines the computation performed for simple testing."""
assert self.with_bbox, 'Bbox head must be implemented.'
if isinstance(x, tuple):
x_shape = x[0].shape
else:
x_shape = x.shape
assert x_shape[0] == 1, 'only accept 1 sample at test mode'
assert x_shape[0] == len(img_metas) == len(proposal_list)
det_bboxes, det_labels = self.simple_test_bboxes(x,
img_metas,
proposal_list,
self.test_cfg,
rescale=rescale)
bbox_results = bbox2result(det_bboxes,
det_labels,
self.bbox_head.num_classes,
thr=self.test_cfg.action_thr)
return [bbox_results]
