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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import unittest
import torch
from detectron2.config import get_cfg
from detectron2.layers import ShapeSpec
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator, RotatedAnchorGenerator
logger = logging.getLogger(__name__)
class TestAnchorGenerator(unittest.TestCase):
def test_default_anchor_generator(self):
cfg = get_cfg()
cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]]
anchor_generator = DefaultAnchorGenerator(cfg, [ShapeSpec(stride=4)])
# only the last two dimensions of features matter here
num_images = 2
features = {"stage3": torch.rand(num_images, 96, 1, 2)}
anchors = anchor_generator([features["stage3"]])
expected_anchor_tensor = torch.tensor(
[
[-32.0, -8.0, 32.0, 8.0],
[-16.0, -16.0, 16.0, 16.0],
[-8.0, -32.0, 8.0, 32.0],
[-64.0, -16.0, 64.0, 16.0],
[-32.0, -32.0, 32.0, 32.0],
[-16.0, -64.0, 16.0, 64.0],
[-28.0, -8.0, 36.0, 8.0], # -28.0 == -32.0 + STRIDE (4)
[-12.0, -16.0, 20.0, 16.0],
[-4.0, -32.0, 12.0, 32.0],
[-60.0, -16.0, 68.0, 16.0],
[-28.0, -32.0, 36.0, 32.0],
[-12.0, -64.0, 20.0, 64.0],
]
)
assert torch.allclose(anchors[0].tensor, expected_anchor_tensor)
def test_default_anchor_generator_centered(self):
# test explicit args
anchor_generator = DefaultAnchorGenerator(
sizes=[32, 64], aspect_ratios=[0.25, 1, 4], strides=[4]
)
# only the last two dimensions of features matter here
num_images = 2
features = {"stage3": torch.rand(num_images, 96, 1, 2)}
expected_anchor_tensor = torch.tensor(
[
[-30.0, -6.0, 34.0, 10.0],
[-14.0, -14.0, 18.0, 18.0],
[-6.0, -30.0, 10.0, 34.0],
[-62.0, -14.0, 66.0, 18.0],
[-30.0, -30.0, 34.0, 34.0],
[-14.0, -62.0, 18.0, 66.0],
[-26.0, -6.0, 38.0, 10.0],
[-10.0, -14.0, 22.0, 18.0],
[-2.0, -30.0, 14.0, 34.0],
[-58.0, -14.0, 70.0, 18.0],
[-26.0, -30.0, 38.0, 34.0],
[-10.0, -62.0, 22.0, 66.0],
]
)
anchors = anchor_generator([features["stage3"]])
assert torch.allclose(anchors[0].tensor, expected_anchor_tensor)
# doesn't work yet
# anchors = torch.jit.script(anchor_generator)([features["stage3"]])
# assert torch.allclose(anchors[0].tensor, expected_anchor_tensor)
def test_rrpn_anchor_generator(self):
cfg = get_cfg()
cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]]
cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [0, 45] # test single list[float]
anchor_generator = RotatedAnchorGenerator(cfg, [ShapeSpec(stride=4)])
# only the last two dimensions of features matter here
num_images = 2
features = {"stage3": torch.rand(num_images, 96, 1, 2)}
anchors = anchor_generator([features["stage3"]])
expected_anchor_tensor = torch.tensor(
[
[0.0, 0.0, 64.0, 16.0, 0.0],
[0.0, 0.0, 64.0, 16.0, 45.0],
[0.0, 0.0, 32.0, 32.0, 0.0],
[0.0, 0.0, 32.0, 32.0, 45.0],
[0.0, 0.0, 16.0, 64.0, 0.0],
[0.0, 0.0, 16.0, 64.0, 45.0],
[0.0, 0.0, 128.0, 32.0, 0.0],
[0.0, 0.0, 128.0, 32.0, 45.0],
[0.0, 0.0, 64.0, 64.0, 0.0],
[0.0, 0.0, 64.0, 64.0, 45.0],
[0.0, 0.0, 32.0, 128.0, 0.0],
[0.0, 0.0, 32.0, 128.0, 45.0],
[4.0, 0.0, 64.0, 16.0, 0.0], # 4.0 == 0.0 + STRIDE (4)
[4.0, 0.0, 64.0, 16.0, 45.0],
[4.0, 0.0, 32.0, 32.0, 0.0],
[4.0, 0.0, 32.0, 32.0, 45.0],
[4.0, 0.0, 16.0, 64.0, 0.0],
[4.0, 0.0, 16.0, 64.0, 45.0],
[4.0, 0.0, 128.0, 32.0, 0.0],
[4.0, 0.0, 128.0, 32.0, 45.0],
[4.0, 0.0, 64.0, 64.0, 0.0],
[4.0, 0.0, 64.0, 64.0, 45.0],
[4.0, 0.0, 32.0, 128.0, 0.0],
[4.0, 0.0, 32.0, 128.0, 45.0],
]
)
assert torch.allclose(anchors[0].tensor, expected_anchor_tensor)
if __name__ == "__main__":
unittest.main()
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