def test_anchor_generator_with_tuples():
from mmdet.core.anchor import build_anchor_generator
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
anchor_generator_cfg = dict(type='SSDAnchorGenerator',
scale_major=False,
input_size=300,
basesize_ratio_range=(0.15, 0.9),
strides=[8, 16, 32, 64, 100, 300],
ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
anchor_generator = build_anchor_generator(anchor_generator_cfg)
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
anchor_generator_cfg_tuples = dict(type='SSDAnchorGenerator',
scale_major=False,
input_size=300,
basesize_ratio_range=(0.15, 0.9),
strides=[(8, 8), (16, 16), (32, 32),
(64, 64), (100, 100),
(300, 300)],
ratios=[[2], [2, 3], [2, 3], [2, 3],
[2], [2]])
anchor_generator_tuples = build_anchor_generator(
anchor_generator_cfg_tuples)
anchors_tuples = anchor_generator_tuples.grid_anchors(
featmap_sizes, device)
for anchor, anchor_tuples in zip(anchors, anchors_tuples):
assert torch.equal(anchor, anchor_tuples)
def test_standard_anchor_generator():
from mmdet.core.anchor import build_anchor_generator
anchor_generator_cfg = dict(type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8])
anchor_generator = build_anchor_generator(anchor_generator_cfg)
assert anchor_generator is not None
def __init__(self,
anchor_generator,
in_channels,
kernel_size=3,
norm_cfg=dict(type='BN'),
weighted_sum=False,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[1., 1., 1., 1.]),
loss_cls=dict(
type='CrossEntropyLoss', reduction='sum',
loss_weight=1.0),
loss_bbox=dict(
type='L1Loss', reduction='sum', loss_weight=1.2),
train_cfg=None,
test_cfg=None,
*args,
**kwargs):
super(SiameseRPNHead, self).__init__(*args, **kwargs)
self.anchor_generator = build_anchor_generator(anchor_generator)
self.bbox_coder = build_bbox_coder(bbox_coder)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.assigner = build_assigner(self.train_cfg.assigner)
self.sampler = build_sampler(self.train_cfg.sampler)
self.cls_heads = nn.ModuleList()
self.reg_heads = nn.ModuleList()
for i in range(len(in_channels)):
self.cls_heads.append(
CorrelationHead(in_channels[i], in_channels[i],
2 * self.anchor_generator.num_base_anchors[0],
kernel_size, norm_cfg))
self.reg_heads.append(
CorrelationHead(in_channels[i], in_channels[i],
4 * self.anchor_generator.num_base_anchors[0],
kernel_size, norm_cfg))
self.weighted_sum = weighted_sum
if self.weighted_sum:
self.cls_weight = nn.Parameter(torch.ones(len(in_channels)))
self.reg_weight = nn.Parameter(torch.ones(len(in_channels)))
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
def test_yolo_anchor_generator():
from mmdet.core.anchor import build_anchor_generator
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
anchor_generator_cfg = dict(type='YOLOAnchorGenerator',
strides=[32, 16, 8],
base_sizes=[
[(116, 90), (156, 198), (373, 326)],
[(30, 61), (62, 45), (59, 119)],
[(10, 13), (16, 30), (33, 23)],
])
featmap_sizes = [(14, 18), (28, 36), (56, 72)]
anchor_generator = build_anchor_generator(anchor_generator_cfg)
# check base anchors
expected_base_anchors = [
torch.Tensor([[-42.0000, -29.0000, 74.0000, 61.0000],
[-62.0000, -83.0000, 94.0000, 115.0000],
[-170.5000, -147.0000, 202.5000, 179.0000]]),
torch.Tensor([[-7.0000, -22.5000, 23.0000, 38.5000],
[-23.0000, -14.5000, 39.0000, 30.5000],
[-21.5000, -51.5000, 37.5000, 67.5000]]),
torch.Tensor([[-1.0000, -2.5000, 9.0000, 10.5000],
[-4.0000, -11.0000, 12.0000, 19.0000],
[-12.5000, -7.5000, 20.5000, 15.5000]])
]
base_anchors = anchor_generator.base_anchors
for i, base_anchor in enumerate(base_anchors):
assert base_anchor.allclose(expected_base_anchors[i])
# check number of base anchors for each level
assert anchor_generator.num_base_anchors == [3, 3, 3]
# check anchor generation
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
assert len(anchors) == 3
def main():
anchor_generator_cfg = dict(type="AnchorGenerator",
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64])
anchor_generator: AnchorGenerator = build_anchor_generator(
anchor_generator_cfg)
multi_level_anchors = anchor_generator.grid_anchors(
featmap_sizes=[
torch.Size([256, 256]),
torch.Size([128, 128]),
torch.Size([64, 64]),
torch.Size([32, 32]),
torch.Size([16, 16]),
],
device="cpu",
)
anchors = torch.cat(multi_level_anchors).numpy()
widths = anchors[:, 2] - anchors[:, 0]
heights = anchors[:, 3] - anchors[:, 1]
data = np.stack([heights, widths], axis=1)
clusters = kmeans(data, k=50)
print(f"aspect rations: {clusters[: 0] / clusters[: 1]}")
print(f"sizes: {np.sqrt(clusters[: 0] * clusters[: 1])}")
def test_ssd_anchor_generator():
from mmdet.core.anchor import build_anchor_generator
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
anchor_generator_cfg = dict(type='SSDAnchorGenerator',
scale_major=False,
input_size=300,
basesize_ratio_range=(0.15, 0.9),
strides=[8, 16, 32, 64, 100, 300],
ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
anchor_generator = build_anchor_generator(anchor_generator_cfg)
# check base anchors
expected_base_anchors = [
torch.Tensor([[-6.5000, -6.5000, 14.5000, 14.5000],
[-11.3704, -11.3704, 19.3704, 19.3704],
[-10.8492, -3.4246, 18.8492, 11.4246],
[-3.4246, -10.8492, 11.4246, 18.8492]]),
torch.Tensor([[-14.5000, -14.5000, 30.5000, 30.5000],
[-25.3729, -25.3729, 41.3729, 41.3729],
[-23.8198, -7.9099, 39.8198, 23.9099],
[-7.9099, -23.8198, 23.9099, 39.8198],
[-30.9711, -4.9904, 46.9711, 20.9904],
[-4.9904, -30.9711, 20.9904, 46.9711]]),
torch.Tensor([[-33.5000, -33.5000, 65.5000, 65.5000],
[-45.5366, -45.5366, 77.5366, 77.5366],
[-54.0036, -19.0018, 86.0036, 51.0018],
[-19.0018, -54.0036, 51.0018, 86.0036],
[-69.7365, -12.5788, 101.7365, 44.5788],
[-12.5788, -69.7365, 44.5788, 101.7365]]),
torch.Tensor([[-44.5000, -44.5000, 108.5000, 108.5000],
[-56.9817, -56.9817, 120.9817, 120.9817],
[-76.1873, -22.0937, 140.1873, 86.0937],
[-22.0937, -76.1873, 86.0937, 140.1873],
[-100.5019, -12.1673, 164.5019, 76.1673],
[-12.1673, -100.5019, 76.1673, 164.5019]]),
torch.Tensor([[-53.5000, -53.5000, 153.5000, 153.5000],
[-66.2185, -66.2185, 166.2185, 166.2185],
[-96.3711, -23.1855, 196.3711, 123.1855],
[-23.1855, -96.3711, 123.1855, 196.3711]]),
torch.Tensor([[19.5000, 19.5000, 280.5000, 280.5000],
[6.6342, 6.6342, 293.3658, 293.3658],
[-34.5549, 57.7226, 334.5549, 242.2774],
[57.7226, -34.5549, 242.2774, 334.5549]]),
]
base_anchors = anchor_generator.base_anchors
for i, base_anchor in enumerate(base_anchors):
assert base_anchor.allclose(expected_base_anchors[i])
# check valid flags
expected_valid_pixels = [5776, 2166, 600, 150, 36, 4]
multi_level_valid_flags = anchor_generator.valid_flags(
featmap_sizes, (300, 300), device)
for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
assert single_level_valid_flag.sum() == expected_valid_pixels[i]
# check number of base anchors for each level
assert anchor_generator.num_base_anchors == [4, 6, 6, 6, 4, 4]
# check anchor generation
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
assert len(anchors) == 6
def test_ssd_anchor_generator():
from mmdet.core.anchor import build_anchor_generator
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
# min_sizes max_sizes must set at the same time
with pytest.raises(AssertionError):
anchor_generator_cfg = dict(type='SSDAnchorGenerator',
scale_major=False,
min_sizes=[48, 100, 150, 202, 253, 300],
max_sizes=None,
strides=[8, 16, 32, 64, 100, 300],
ratios=[[2], [2, 3], [2, 3], [2, 3], [2],
[2]])
build_anchor_generator(anchor_generator_cfg)
# length of min_sizes max_sizes must be the same
with pytest.raises(AssertionError):
anchor_generator_cfg = dict(type='SSDAnchorGenerator',
scale_major=False,
min_sizes=[48, 100, 150, 202, 253, 300],
max_sizes=[100, 150, 202, 253],
strides=[8, 16, 32, 64, 100, 300],
ratios=[[2], [2, 3], [2, 3], [2, 3], [2],
[2]])
build_anchor_generator(anchor_generator_cfg)
# test setting anchor size manually
anchor_generator_cfg = dict(type='SSDAnchorGenerator',
scale_major=False,
min_sizes=[48, 100, 150, 202, 253, 304],
max_sizes=[100, 150, 202, 253, 304, 320],
strides=[16, 32, 64, 107, 160, 320],
ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3],
[2, 3]])
featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
anchor_generator = build_anchor_generator(anchor_generator_cfg)
expected_base_anchors = [
torch.Tensor([[-16.0000, -16.0000, 32.0000, 32.0000],
[-26.6410, -26.6410, 42.6410, 42.6410],
[-25.9411, -8.9706, 41.9411, 24.9706],
[-8.9706, -25.9411, 24.9706, 41.9411],
[-33.5692, -5.8564, 49.5692, 21.8564],
[-5.8564, -33.5692, 21.8564, 49.5692]]),
torch.Tensor([[-34.0000, -34.0000, 66.0000, 66.0000],
[-45.2372, -45.2372, 77.2372, 77.2372],
[-54.7107, -19.3553, 86.7107, 51.3553],
[-19.3553, -54.7107, 51.3553, 86.7107],
[-70.6025, -12.8675, 102.6025, 44.8675],
[-12.8675, -70.6025, 44.8675, 102.6025]]),
torch.Tensor([[-43.0000, -43.0000, 107.0000, 107.0000],
[-55.0345, -55.0345, 119.0345, 119.0345],
[-74.0660, -21.0330, 138.0660, 85.0330],
[-21.0330, -74.0660, 85.0330, 138.0660],
[-97.9038, -11.3013, 161.9038, 75.3013],
[-11.3013, -97.9038, 75.3013, 161.9038]]),
torch.Tensor([[-47.5000, -47.5000, 154.5000, 154.5000],
[-59.5332, -59.5332, 166.5332, 166.5332],
[-89.3356, -17.9178, 196.3356, 124.9178],
[-17.9178, -89.3356, 124.9178, 196.3356],
[-121.4371, -4.8124, 228.4371, 111.8124],
[-4.8124, -121.4371, 111.8124, 228.4371]]),
torch.Tensor([[-46.5000, -46.5000, 206.5000, 206.5000],
[-58.6651, -58.6651, 218.6651, 218.6651],
[-98.8980, -9.4490, 258.8980, 169.4490],
[-9.4490, -98.8980, 169.4490, 258.8980],
[-139.1044, 6.9652, 299.1044, 153.0348],
[6.9652, -139.1044, 153.0348, 299.1044]]),
torch.Tensor([[8.0000, 8.0000, 312.0000, 312.0000],
[4.0513, 4.0513, 315.9487, 315.9487],
[-54.9605, 52.5198, 374.9604, 267.4802],
[52.5198, -54.9605, 267.4802, 374.9604],
[-103.2717, 72.2428, 423.2717, 247.7572],
[72.2428, -103.2717, 247.7572, 423.2717]])
]
base_anchors = anchor_generator.base_anchors
for i, base_anchor in enumerate(base_anchors):
assert base_anchor.allclose(expected_base_anchors[i])
# check valid flags
expected_valid_pixels = [2400, 600, 150, 54, 24, 6]
multi_level_valid_flags = anchor_generator.valid_flags(
featmap_sizes, (320, 320), device)
for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
assert single_level_valid_flag.sum() == expected_valid_pixels[i]
# check number of base anchors for each level
assert anchor_generator.num_base_anchors == [6, 6, 6, 6, 6, 6]
# check anchor generation
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
assert len(anchors) == 6
# test vgg ssd anchor setting
anchor_generator_cfg = dict(type='SSDAnchorGenerator',
scale_major=False,
input_size=300,
basesize_ratio_range=(0.15, 0.9),
strides=[8, 16, 32, 64, 100, 300],
ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
anchor_generator = build_anchor_generator(anchor_generator_cfg)
# check base anchors
expected_base_anchors = [
torch.Tensor([[-6.5000, -6.5000, 14.5000, 14.5000],
[-11.3704, -11.3704, 19.3704, 19.3704],
[-10.8492, -3.4246, 18.8492, 11.4246],
[-3.4246, -10.8492, 11.4246, 18.8492]]),
torch.Tensor([[-14.5000, -14.5000, 30.5000, 30.5000],
[-25.3729, -25.3729, 41.3729, 41.3729],
[-23.8198, -7.9099, 39.8198, 23.9099],
[-7.9099, -23.8198, 23.9099, 39.8198],
[-30.9711, -4.9904, 46.9711, 20.9904],
[-4.9904, -30.9711, 20.9904, 46.9711]]),
torch.Tensor([[-33.5000, -33.5000, 65.5000, 65.5000],
[-45.5366, -45.5366, 77.5366, 77.5366],
[-54.0036, -19.0018, 86.0036, 51.0018],
[-19.0018, -54.0036, 51.0018, 86.0036],
[-69.7365, -12.5788, 101.7365, 44.5788],
[-12.5788, -69.7365, 44.5788, 101.7365]]),
torch.Tensor([[-44.5000, -44.5000, 108.5000, 108.5000],
[-56.9817, -56.9817, 120.9817, 120.9817],
[-76.1873, -22.0937, 140.1873, 86.0937],
[-22.0937, -76.1873, 86.0937, 140.1873],
[-100.5019, -12.1673, 164.5019, 76.1673],
[-12.1673, -100.5019, 76.1673, 164.5019]]),
torch.Tensor([[-53.5000, -53.5000, 153.5000, 153.5000],
[-66.2185, -66.2185, 166.2185, 166.2185],
[-96.3711, -23.1855, 196.3711, 123.1855],
[-23.1855, -96.3711, 123.1855, 196.3711]]),
torch.Tensor([[19.5000, 19.5000, 280.5000, 280.5000],
[6.6342, 6.6342, 293.3658, 293.3658],
[-34.5549, 57.7226, 334.5549, 242.2774],
[57.7226, -34.5549, 242.2774, 334.5549]]),
]
base_anchors = anchor_generator.base_anchors
for i, base_anchor in enumerate(base_anchors):
assert base_anchor.allclose(expected_base_anchors[i])
# check valid flags
expected_valid_pixels = [5776, 2166, 600, 150, 36, 4]
multi_level_valid_flags = anchor_generator.valid_flags(
featmap_sizes, (300, 300), device)
for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
assert single_level_valid_flag.sum() == expected_valid_pixels[i]
# check number of base anchors for each level
assert anchor_generator.num_base_anchors == [4, 6, 6, 6, 4, 4]
# check anchor generation
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
assert len(anchors) == 6
