Add experimental VGG-style resnet50 backbone.

Author: datumbox

torchvision/models/detection/ssd.py

@@ -587,12 +587,34 @@ def __init__(self, backbone: resnet.ResNet):
         backbone_out_channels = self.features[-1][-1].bn3.num_features
         extra = nn.ModuleList([
             nn.Sequential(
-                nn.Conv2d(backbone_out_channels, 256, kernel_size=3, padding=1, stride=2, bias=False),
+                nn.Conv2d(backbone_out_channels, 256, kernel_size=1, bias=False),
+                nn.BatchNorm2d(256),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=2, bias=False),
+                nn.BatchNorm2d(512),
+                nn.ReLU(inplace=True),
+            ),
+            nn.Sequential(
+                nn.Conv2d(512, 128, kernel_size=1, bias=False),
+                nn.BatchNorm2d(128),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2, bias=False),
                 nn.BatchNorm2d(256),
                 nn.ReLU(inplace=True),
             ),
             nn.Sequential(
-                nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=2, bias=False),
+                nn.Conv2d(256, 128, kernel_size=1, bias=False),
+                nn.BatchNorm2d(128),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(128, 256, kernel_size=3, bias=False),
+                nn.BatchNorm2d(256),
+                nn.ReLU(inplace=True),
+            ),
+            nn.Sequential(
+                nn.Conv2d(256, 128, kernel_size=1, bias=False),
+                nn.BatchNorm2d(128),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(128, 256, kernel_size=2, bias=False),
                 nn.BatchNorm2d(256),
                 nn.ReLU(inplace=True),
             ),
@@ -636,7 +658,9 @@ def ssd512_resnet50(pretrained: bool = False, progress: bool = True, num_classes
         pretrained_backbone = False
 
     backbone = _resnet_extractor("resnet50", pretrained_backbone, trainable_backbone_layers)
-    anchor_generator = DefaultBoxGenerator([[2], [2, 3], [2, 3], [2, 3], [2]], min_ratio=0.04)
+    anchor_generator = DefaultBoxGenerator([[2], [2, 3], [2, 3], [2, 3], [2], [2], [2]],
+                                           scales=[0.04, 0.1, 0.26, 0.42, 0.58, 0.74, 0.9, 1.06],
+                                           steps=[8, 16, 32, 64, 128, 256, 512])
     model = SSD(backbone, anchor_generator, (512, 512), num_classes, **kwargs)
     if pretrained:
         weights_name = 'ssd512_resnet50_coco'