Simplify the setup for AnchorGenerator in unittest (#3023)

test/test_models_detection_anchor_utils.py

 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)
@@ -16,26 +16,20 @@ class Tester(unittest.TestCase):
         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)
 
@@ -43,13 +37,25 @@ class Tester(unittest.TestCase):
         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)