PyTorch 1.11.0 compatibility updates

Resolves `AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor'` first raised in ultralytics/yolov5#5499 and observed in all CI runs on just-released PyTorch 1.11.0.

models/experimental.py

@@ -94,21 +94,22 @@ 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())  # fused or un-fused model in eval mode
 
     # Compatibility updates
     for m in model.modules():
-        if type(m) 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:
+        t = type(m)
+        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
+            m.inplace = inplace  # torch 1.7.0 compatibility
+            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:
-            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
@@ -118,3 +119,4 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
             setattr(model, k, getattr(model[-1], k))
         model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
         return model  # return ensemble
+