fuse update

detect.py

@@ -21,13 +21,10 @@ def detect(save_img=False):
 
     # Load model
     google_utils.attempt_download(weights)
-    model = torch.load(weights, map_location=device)['model'].float()  # load to FP32
-    # torch.save(torch.load(weights, map_location=device), weights)  # update model if SourceChangeWarning
-    # model.fuse()
-    model.to(device).eval()
-    imgsz = check_img_size(imgsz, s=model.model[-1].stride.max())  # check img_size
+    model = torch.load(weights, map_location=device)['model'].float().eval()  # load FP32 model
+    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
     if half:
-        model.half()  # to FP16
+        model.float()  # to FP16
 
     # Second-stage classifier
     classify = False

models/yolo.py

@@ -142,14 +142,14 @@ class Model(nn.Module):
     #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
 
     def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        print('Fusing layers...')
+        print('Fusing layers... ', end='')
         for m in self.model.modules():
             if type(m) is Conv:
                 m.conv = torch_utils.fuse_conv_and_bn(m.conv, m.bn)  # update conv
                 m.bn = None  # remove batchnorm
                 m.forward = m.fuseforward  # update forward
         torch_utils.model_info(self)
-
+        return self
 
 def parse_model(md, ch):  # model_dict, input_channels(3)
     print('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))

test.py

@@ -22,6 +22,7 @@ def test(data,
     # Initialize/load model and set device
     if model is None:
         training = False
+        merge = opt.merge  # use Merge NMS
         device = torch_utils.select_device(opt.device, batch_size=batch_size)
 
         # Remove previous
@@ -59,7 +60,6 @@ def test(data,
 
     # Dataloader
     if dataloader is None:  # not training
-        merge = opt.merge  # use Merge NMS
         img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
         _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
         path = data['test'] if opt.task == 'test' else data['val']  # path to val/test images