Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Simplify the setup for AnchorGenerator in unittest #3023

Merged
merged 1 commit into from
Nov 19, 2020
Merged

Simplify the setup for AnchorGenerator in unittest #3023

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
38 changes: 22 additions & 16 deletions test/test_models_detection_anchor_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,11 +1,11 @@
from collections import OrderedDict
import torch
import unittest
from common_utils import TestCase
from torchvision.models.detection.anchor_utils import AnchorGenerator
from torchvision.models.detection.image_list import ImageList


class Tester(unittest.TestCase):
class Tester(TestCase):
def test_incorrect_anchors(self):
incorrect_sizes = ((2, 4, 8), (32, 8), )
incorrect_aspects = (0.5, 1.0)
Expand All @@ -16,40 +16,46 @@ def test_incorrect_anchors(self):
self.assertRaises(ValueError, anc, image_list, feature_maps)

def _init_test_anchor_generator(self):
anchor_sizes = tuple((x,) for x in [32, 64, 128])
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
anchor_sizes = ((10,),)
aspect_ratios = ((1,),)
anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)

return anchor_generator

def get_features(self, images):
s0, s1 = images.shape[-2:]
features = [
('0', torch.rand(2, 8, s0 // 4, s1 // 4)),
('1', torch.rand(2, 16, s0 // 8, s1 // 8)),
('2', torch.rand(2, 32, s0 // 16, s1 // 16)),
]
features = OrderedDict(features)
features = [torch.rand(2, 8, s0 // 5, s1 // 5)]
return features

def test_anchor_generator(self):
images = torch.randn(2, 3, 16, 32)
images = torch.randn(2, 3, 15, 15)
features = self.get_features(images)
features = list(features.values())
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)

# Compute target anchors numbers
# 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

self.assertEqual(num_anchors_estimated, 126)
anchors_output = torch.tensor([[-5., -5., 5., 5.],
[0., -5., 10., 5.],
[5., -5., 15., 5.],
[-5., 0., 5., 10.],
[0., 0., 10., 10.],
[5., 0., 15., 10.],
[-5., 5., 5., 15.],
[0., 5., 10., 15.],
[5., 5., 15., 15.]])

self.assertEqual(num_anchors_estimated, 9)
self.assertEqual(len(anchors), 2)
self.assertEqual(tuple(anchors[0].shape), (num_anchors_estimated, 4))
self.assertEqual(tuple(anchors[1].shape), (num_anchors_estimated, 4))
self.assertEqual(tuple(anchors[0].shape), (9, 4))
self.assertEqual(tuple(anchors[1].shape), (9, 4))
self.assertEqual(anchors[0], anchors_output)
self.assertEqual(anchors[1], anchors_output)