fix upsampling err with torch 1.11+

requirements.txt

@@ -5,8 +5,8 @@ opencv-python-headless
 Pillow
 PyYAML>=5.3.1
 scipy>=1.4.1
-torch==1.10.1
-torchvision==0.11.2
+torch>=1.7.0
+torchvision>=0.8.1
 tqdm>=4.41.0
 
 # logging -------------------------------------

yolov5/models/experimental.py

@@ -94,21 +94,23 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
         ckpt = torch.load(attempt_download(w), map_location=map_location)  # load
-        if fuse:
-            model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
-        else:
-            model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval())  # without layer fuse
-
+        ckpt = (ckpt['ema'] or ckpt['model']).float()  # FP32 model
+        model.append(ckpt.fuse().eval() if fuse else ckpt.eval())
     # Compatibility updates
     for m in model.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
+        t = type(m)
+        if t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
             m.inplace = inplace  # pytorch 1.7.0 compatibility
-            if type(m) is Detect:
+            if t is Detect:
                 if not isinstance(m.anchor_grid, list):  # new Detect Layer compatibility
                     delattr(m, 'anchor_grid')
                     setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
-        elif type(m) is Conv:
+        elif t is Conv:
             m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
+        elif t is nn.Upsample:
+            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
+        elif t is Conv:
+            m._non_persistent_buffers_set = set()  # torch 1.6.0 compatibility
 
     if len(model) == 1:
         return model[-1]  # return model