Fuse Conv2d + BatchNorm2d

detect.py

@@ -34,21 +34,8 @@ def detect(
     else:  # darknet format
         _ = load_darknet_weights(model, weights)
 
-    # Fuse batchnorm
-    fuse = True
-    if fuse:
-        fused_list = nn.ModuleList()
-        for a in list(model.children())[0]:
-            for i, b in enumerate(a):
-                if isinstance(b, nn.modules.batchnorm.BatchNorm2d):
-                    # fuse this bn layer with the previous conv2d layer
-                    conv = a[i - 1]
-                    fused = torch_utils.fuse_conv_and_bn(conv, b)
-                    a = nn.Sequential(fused, *list(a.children())[i + 1:])
-                    break
-            fused_list.append(a)
-        model.module_list = fused_list
-        #model_info(model)  # yolov3-spp reduced from 225 to 152 layers
+    # Fuse Conv2d + BatchNorm2d layers
+    model.fuse()
 
     model.to(device).eval()
 

models.py

@@ -212,6 +212,21 @@ class Darknet(nn.Module):
             io, p = list(zip(*output))  # inference output, training output
             return torch.cat(io, 1), p
 
+    def fuse(self):
+        # Fuse Conv2d + BatchNorm2d layers throughout model
+        fused_list = nn.ModuleList()
+        for a in list(self.children())[0]:
+            for i, b in enumerate(a):
+                if isinstance(b, nn.modules.batchnorm.BatchNorm2d):
+                    # fuse this bn layer with the previous conv2d layer
+                    conv = a[i - 1]
+                    fused = torch_utils.fuse_conv_and_bn(conv, b)
+                    a = nn.Sequential(fused, *list(a.children())[i + 1:])
+                    break
+            fused_list.append(a)
+        self.module_list = fused_list
+        # model_info(self)  # yolov3-spp reduced from 225 to 152 layers
+
 
 def get_yolo_layers(model):
     a = [module_def['type'] == 'yolo' for module_def in model.module_defs]