def test_anchor_3d_range_generator():
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
anchor_generator_cfg = dict(type='Anchor3DRangeGenerator',
ranges=[
[0, -39.68, -0.6, 70.4, 39.68, -0.6],
[0, -39.68, -0.6, 70.4, 39.68, -0.6],
[0, -39.68, -1.78, 70.4, 39.68, -1.78],
],
sizes=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73],
[1.6, 3.9, 1.56]],
rotations=[0, 1.57],
reshape_out=False)
anchor_generator = build_anchor_generator(anchor_generator_cfg)
repr_str = repr(anchor_generator)
expected_repr_str = 'Anchor3DRangeGenerator(anchor_range=' \
'[[0, -39.68, -0.6, 70.4, 39.68, -0.6], ' \
'[0, -39.68, -0.6, 70.4, 39.68, -0.6], ' \
'[0, -39.68, -1.78, 70.4, 39.68, -1.78]],' \
'\nscales=[1],\nsizes=[[0.6, 0.8, 1.73], ' \
'[0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],' \
'\nrotations=[0, 1.57],\nreshape_out=False,' \
'\nsize_per_range=True)'
assert repr_str == expected_repr_str
featmap_size = (256, 256)
mr_anchors = anchor_generator.single_level_grid_anchors(featmap_size,
1.1,
device=device)
assert mr_anchors.shape == torch.Size([1, 256, 256, 3, 2, 7])
def test_aligned_anchor_generator_per_cls():
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
anchor_generator_cfg = dict(
type='AlignedAnchor3DRangeGeneratorPerCls',
ranges=[[-100, -100, -1.80, 100, 100, -1.80],
[-100, -100, -1.30, 100, 100, -1.30]],
sizes=[[0.63, 1.76, 1.44], [0.96, 2.35, 1.59]],
custom_values=[0, 0],
rotations=[0, 1.57],
reshape_out=False)
featmap_sizes = [(100, 100), (50, 50)]
anchor_generator = build_anchor_generator(anchor_generator_cfg)
# check base anchors
expected_grid_anchors = [[
torch.tensor([[
-99.0000, -99.0000, -1.8000, 0.6300, 1.7600, 1.4400, 0.0000,
0.0000, 0.0000
],
[
-99.0000, -99.0000, -1.8000, 0.6300, 1.7600, 1.4400,
1.5700, 0.0000, 0.0000
]],
device=device),
torch.tensor([[
-98.0000, -98.0000, -1.3000, 0.9600, 2.3500, 1.5900, 0.0000,
0.0000, 0.0000
],
[
-98.0000, -98.0000, -1.3000, 0.9600, 2.3500, 1.5900,
1.5700, 0.0000, 0.0000
]],
device=device)
]]
multi_level_anchors = anchor_generator.grid_anchors(
featmap_sizes, device=device)
expected_multi_level_shapes = [[
torch.Size([20000, 9]), torch.Size([5000, 9])
]]
for i, single_level_anchor in enumerate(multi_level_anchors):
assert len(single_level_anchor) == len(expected_multi_level_shapes[i])
# set [:2*interval:interval] thus it could cover
# 2 (len(size) * len(rotations)) anchors on 2 location
# Note that len(size) for each class is always 1 in this case
for j in range(len(single_level_anchor)):
interval = int(expected_multi_level_shapes[i][j][0] / 2)
assert single_level_anchor[j][:2 * interval:interval].allclose(
expected_grid_anchors[i][j])
def test_aligned_anchor_generator():
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
anchor_generator_cfg = dict(
type='AlignedAnchor3DRangeGenerator',
ranges=[[-51.2, -51.2, -1.80, 51.2, 51.2, -1.80]],
scales=[1, 2, 4],
sizes=[
[0.8660, 2.5981, 1.], # 1.5/sqrt(3)
[0.5774, 1.7321, 1.], # 1/sqrt(3)
[1., 1., 1.],
[0.4, 0.4, 1],
],
custom_values=[0, 0],
rotations=[0, 1.57],
size_per_range=False,
reshape_out=True)
featmap_sizes = [(256, 256), (128, 128), (64, 64)]
anchor_generator = build_anchor_generator(anchor_generator_cfg)
assert anchor_generator.num_base_anchors == 8
# check base anchors
expected_grid_anchors = [
torch.tensor([[
-51.0000, -51.0000, -1.8000, 0.8660, 2.5981, 1.0000, 0.0000,
0.0000, 0.0000
],
[
-51.0000, -51.0000, -1.8000, 0.4000, 0.4000, 1.0000,
1.5700, 0.0000, 0.0000
],
[
-50.6000, -51.0000, -1.8000, 0.4000, 0.4000, 1.0000,
0.0000, 0.0000, 0.0000
],
[
-50.2000, -51.0000, -1.8000, 1.0000, 1.0000, 1.0000,
1.5700, 0.0000, 0.0000
],
[
-49.8000, -51.0000, -1.8000, 1.0000, 1.0000, 1.0000,
0.0000, 0.0000, 0.0000
],
[
-49.4000, -51.0000, -1.8000, 0.5774, 1.7321, 1.0000,
1.5700, 0.0000, 0.0000
],
[
-49.0000, -51.0000, -1.8000, 0.5774, 1.7321, 1.0000,
0.0000, 0.0000, 0.0000
],
[
-48.6000, -51.0000, -1.8000, 0.8660, 2.5981, 1.0000,
1.5700, 0.0000, 0.0000
]],
device=device),
torch.tensor([[
-50.8000, -50.8000, -1.8000, 1.7320, 5.1962, 2.0000, 0.0000,
0.0000, 0.0000
],
[
-50.8000, -50.8000, -1.8000, 0.8000, 0.8000, 2.0000,
1.5700, 0.0000, 0.0000
],
[
-50.0000, -50.8000, -1.8000, 0.8000, 0.8000, 2.0000,
0.0000, 0.0000, 0.0000
],
[
-49.2000, -50.8000, -1.8000, 2.0000, 2.0000, 2.0000,
1.5700, 0.0000, 0.0000
],
[
-48.4000, -50.8000, -1.8000, 2.0000, 2.0000, 2.0000,
0.0000, 0.0000, 0.0000
],
[
-47.6000, -50.8000, -1.8000, 1.1548, 3.4642, 2.0000,
1.5700, 0.0000, 0.0000
],
[
-46.8000, -50.8000, -1.8000, 1.1548, 3.4642, 2.0000,
0.0000, 0.0000, 0.0000
],
[
-46.0000, -50.8000, -1.8000, 1.7320, 5.1962, 2.0000,
1.5700, 0.0000, 0.0000
]],
device=device),
torch.tensor([[
-50.4000, -50.4000, -1.8000, 3.4640, 10.3924, 4.0000, 0.0000,
0.0000, 0.0000
],
[
-50.4000, -50.4000, -1.8000, 1.6000, 1.6000, 4.0000,
1.5700, 0.0000, 0.0000
],
[
-48.8000, -50.4000, -1.8000, 1.6000, 1.6000, 4.0000,
0.0000, 0.0000, 0.0000
],
[
-47.2000, -50.4000, -1.8000, 4.0000, 4.0000, 4.0000,
1.5700, 0.0000, 0.0000
],
[
-45.6000, -50.4000, -1.8000, 4.0000, 4.0000, 4.0000,
0.0000, 0.0000, 0.0000
],
[
-44.0000, -50.4000, -1.8000, 2.3096, 6.9284, 4.0000,
1.5700, 0.0000, 0.0000
],
[
-42.4000, -50.4000, -1.8000, 2.3096, 6.9284, 4.0000,
0.0000, 0.0000, 0.0000
],
[
-40.8000, -50.4000, -1.8000, 3.4640, 10.3924, 4.0000,
1.5700, 0.0000, 0.0000
]],
device=device)
]
multi_level_anchors = anchor_generator.grid_anchors(featmap_sizes,
device=device)
expected_multi_level_shapes = [
torch.Size([524288, 9]),
torch.Size([131072, 9]),
torch.Size([32768, 9])
]
for i, single_level_anchor in enumerate(multi_level_anchors):
assert single_level_anchor.shape == expected_multi_level_shapes[i]
# set [:56:7] thus it could cover 8 (len(size) * len(rotations))
# anchors on 8 location
assert single_level_anchor[:56:7].allclose(expected_grid_anchors[i])
