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import pytest
import torch
from common_utils import assert_equal
from torchvision.models.detection.anchor_utils import AnchorGenerator, DefaultBoxGenerator
from torchvision.models.detection.image_list import ImageList
class Tester:
def test_incorrect_anchors(self):
incorrect_sizes = (
(2, 4, 8),
(32, 8),
)
incorrect_aspects = (0.5, 1.0)
anc = AnchorGenerator(incorrect_sizes, incorrect_aspects)
image1 = torch.randn(3, 800, 800)
image_list = ImageList(image1, [(800, 800)])
feature_maps = [torch.randn(1, 50)]
pytest.raises(AssertionError, anc, image_list, feature_maps)
def _init_test_anchor_generator(self):
anchor_sizes = ((10,),)
aspect_ratios = ((1,),)
anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)
return anchor_generator
def _init_test_defaultbox_generator(self):
aspect_ratios = [[2]]
dbox_generator = DefaultBoxGenerator(aspect_ratios)
return dbox_generator
def get_features(self, images):
s0, s1 = images.shape[-2:]
features = [torch.rand(2, 8, s0 // 5, s1 // 5)]
return features
def test_anchor_generator(self):
images = torch.randn(2, 3, 15, 15)
features = self.get_features(images)
image_shapes = [i.shape[-2:] for i in images]
images = ImageList(images, image_shapes)
model = self._init_test_anchor_generator()
model.eval()
anchors = model(images, features)
# Estimate the number of target anchors
grid_sizes = [f.shape[-2:] for f in features]
num_anchors_estimated = 0
for sizes, num_anchors_per_loc in zip(grid_sizes, model.num_anchors_per_location()):
num_anchors_estimated += sizes[0] * sizes[1] * num_anchors_per_loc
anchors_output = torch.tensor(
[
[-5.0, -5.0, 5.0, 5.0],
[0.0, -5.0, 10.0, 5.0],
[5.0, -5.0, 15.0, 5.0],
[-5.0, 0.0, 5.0, 10.0],
[0.0, 0.0, 10.0, 10.0],
[5.0, 0.0, 15.0, 10.0],
[-5.0, 5.0, 5.0, 15.0],
[0.0, 5.0, 10.0, 15.0],
[5.0, 5.0, 15.0, 15.0],
]
)
assert num_anchors_estimated == 9
assert len(anchors) == 2
assert tuple(anchors[0].shape) == (9, 4)
assert tuple(anchors[1].shape) == (9, 4)
assert_equal(anchors[0], anchors_output)
assert_equal(anchors[1], anchors_output)
def test_defaultbox_generator(self):
images = torch.zeros(2, 3, 15, 15)
features = [torch.zeros(2, 8, 1, 1)]
image_shapes = [i.shape[-2:] for i in images]
images = ImageList(images, image_shapes)
model = self._init_test_defaultbox_generator()
model.eval()
dboxes = model(images, features)
dboxes_output = torch.tensor(
[
[6.3750, 6.3750, 8.6250, 8.6250],
[4.7443, 4.7443, 10.2557, 10.2557],
[5.9090, 6.7045, 9.0910, 8.2955],
[6.7045, 5.9090, 8.2955, 9.0910],
]
)
assert len(dboxes) == 2
assert tuple(dboxes[0].shape) == (4, 4)
assert tuple(dboxes[1].shape) == (4, 4)
torch.testing.assert_close(dboxes[0], dboxes_output, rtol=1e-5, atol=1e-8)
torch.testing.assert_close(dboxes[1], dboxes_output, rtol=1e-5, atol=1e-8)
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