Adds Anchor tests with ground-truth outputs (#2983)

* Add AnchorGenerator with ground-truth outputs

* Minor fixes

test/test_models_detection_anchor_utils.py

+from collections import OrderedDict
 import torch
 import unittest
 from torchvision.models.detection.anchor_utils import AnchorGenerator
@@ -13,3 +14,42 @@ class Tester(unittest.TestCase):
         image_list = ImageList(image1, [(800, 800)])
         feature_maps = [torch.randn(1, 50)]
         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_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)
+        return features
+
+    def test_anchor_generator(self):
+        images = torch.randn(2, 3, 16, 32)
+        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
+        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)
+        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))