Pass custom scales on DefaultBoxGenerator and change default estimation. (#3766)

test/test_models_detection_anchor_utils.py

@@ -76,10 +76,10 @@ class Tester(TestCase):
         dboxes = model(images, features)
 
         dboxes_output = torch.tensor([
-            [6.9750, 6.9750, 8.0250, 8.0250],
-            [6.7315, 6.7315, 8.2685, 8.2685],
-            [6.7575, 7.1288, 8.2425, 7.8712],
-            [7.1288, 6.7575, 7.8712, 8.2425]
+            [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]
         ])
 
         self.assertEqual(len(dboxes), 2)

torchvision/models/detection/anchor_utils.py

@@ -138,9 +138,11 @@ class DefaultBoxGenerator(nn.Module):
     Args:
         aspect_ratios (List[List[int]]): A list with all the aspect ratios used in each feature map.
         min_ratio (float): The minimum scale :math:`\text{s}_{\text{min}}` of the default boxes used in the estimation
-            of the scales of each feature map.
+            of the scales of each feature map. It is used only if the ``scales`` parameter is not provided.
         max_ratio (float): The maximum scale :math:`\text{s}_{\text{max}}`  of the default boxes used in the estimation
-            of the scales of each feature map.
+            of the scales of each feature map. It is used only if the ``scales`` parameter is not provided.
+        scales (List[float]], optional): The scales of the default boxes. If not provided it will be estimated using
+            the ``min_ratio`` and ``max_ratio`` parameters.
         steps (List[int]], optional): It's a hyper-parameter that affects the tiling of defalt boxes. If not provided
             it will be estimated from the data.
         clip (bool): Whether the standardized values of default boxes should be clipped between 0 and 1. The clipping
@@ -148,7 +150,7 @@ class DefaultBoxGenerator(nn.Module):
     """
 
     def __init__(self, aspect_ratios: List[List[int]], min_ratio: float = 0.15, max_ratio: float = 0.9,
-                 steps: Optional[List[int]] = None, clip: bool = True):
+                 scales: Optional[List[float]] = None, steps: Optional[List[int]] = None, clip: bool = True):
         super().__init__()
         if steps is not None:
             assert len(aspect_ratios) == len(steps)
@@ -158,15 +160,15 @@ class DefaultBoxGenerator(nn.Module):
         num_outputs = len(aspect_ratios)
 
         # Estimation of default boxes scales
-        # Inspired from https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_pascal.py#L311-L317
-        min_centile = int(100 * min_ratio)
-        max_centile = int(100 * max_ratio)
-        conv4_centile = min_centile // 2  # assume half of min_ratio as in paper
-        step = (max_centile - min_centile) // (num_outputs - 2)
-        centiles = [conv4_centile, min_centile]
-        for c in range(min_centile, max_centile + 1, step):
-            centiles.append(c + step)
-        self.scales = [c / 100 for c in centiles]
+        if scales is None:
+            if num_outputs > 1:
+                range_ratio = max_ratio - min_ratio
+                self.scales = [min_ratio + range_ratio * k / (num_outputs - 1.0) for k in range(num_outputs)]
+                self.scales.append(1.0)
+            else:
+                self.scales = [min_ratio, max_ratio]
+        else:
+            self.scales = scales
 
         self._wh_pairs = []
         for k in range(num_outputs):
@@ -207,9 +209,17 @@ class DefaultBoxGenerator(nn.Module):
         for k, f_k in enumerate(grid_sizes):
             # Now add the default boxes for each width-height pair
             for j in range(f_k[0]):
-                cy = (j + 0.5) / (float(f_k[0]) if self.steps is None else image_size[1] / self.steps[k])
+                if self.steps is not None:
+                    y_f_k = image_size[1] / self.steps[k]
+                else:
+                    y_f_k = float(f_k[0])
+                cy = (j + 0.5) / y_f_k
                 for i in range(f_k[1]):
-                    cx = (i + 0.5) / (float(f_k[1]) if self.steps is None else image_size[0] / self.steps[k])
+                    if self.steps is not None:
+                        x_f_k = image_size[0] / self.steps[k]
+                    else:
+                        x_f_k = float(f_k[1])
+                    cx = (i + 0.5) / x_f_k
                     default_boxes.extend([[cx, cy, w, h] for w, h in self._wh_pairs[k]])
 
         dboxes = []

torchvision/models/detection/ssd.py

@@ -552,7 +552,9 @@ def ssd300_vgg16(pretrained: bool = False, progress: bool = True, num_classes: i
         pretrained_backbone = False
 
     backbone = _vgg_extractor("vgg16_features", False, progress, pretrained_backbone, trainable_backbone_layers, True)
-    anchor_generator = DefaultBoxGenerator([[2], [2, 3], [2, 3], [2, 3], [2], [2]], steps=[8, 16, 32, 64, 100, 300])
+    anchor_generator = DefaultBoxGenerator([[2], [2, 3], [2, 3], [2, 3], [2], [2]],
+                                           scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05],
+                                           steps=[8, 16, 32, 64, 100, 300])
     model = SSD(backbone, anchor_generator, (300, 300), num_classes,
                 image_mean=[0.48235, 0.45882, 0.40784], image_std=[1., 1., 1.], **kwargs)
     if pretrained: