PyTorch 1.6.0 compatability updates

detect.py

@@ -154,7 +154,7 @@ def detect(save_img=False):
 
     with torch.no_grad():
         if opt.update:  # update all models (to fix SourceChangeWarning)
-            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov3-spp.pt']:
+            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                 detect()
                 strip_optimizer(opt.weights)
         else:

models/yolo.py

@@ -90,9 +90,9 @@ def forward(self, x, augment=False, profile=False):
                 yi = self.forward_once(xi)[0]  # forward
                 # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
                 yi[..., :4] /= si  # de-scale
-                if fi is 2:
+                if fi == 2:
                     yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
-                elif fi is 3:
+                elif fi == 3:
                     yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
                 y.append(yi)
             return torch.cat(y, 1), None  # augmented inference, train
@@ -148,6 +148,7 @@ def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
         print('Fusing layers... ', end='')
         for m in self.model.modules():
             if type(m) is Conv:
+                m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatability
                 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

test.py

@@ -148,16 +148,16 @@ def test(data,
 
                 # Per target class
                 for cls in torch.unique(tcls_tensor):
-                    ti = (cls == tcls_tensor).nonzero().view(-1)  # prediction indices
-                    pi = (cls == pred[:, 5]).nonzero().view(-1)  # target indices
+                    ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1)  # prediction indices
+                    pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1)  # target indices
 
                     # Search for detections
                     if pi.shape[0]:
                         # Prediction to target ious
                         ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1)  # best ious, indices
 
                         # Append detections
-                        for j in (ious > iouv[0]).nonzero():
+                        for j in (ious > iouv[0]).nonzero(as_tuple=False):
                             d = ti[i[j]]  # detected target
                             if d not in detected:
                                 detected.append(d)