modify issue AttributeError: Upsample object has no attribute...

modify issue AttributeError: Upsample object has no attribute recompute_scale_factor,torch version>1.10 will get the issue,and pillow version will got some issue

models/experimental.py

@@ -92,22 +92,33 @@ 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
+        #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:
+        #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 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

requirements.txt

@@ -4,7 +4,7 @@
 matplotlib>=3.2.2
 numpy>=1.18.5
 opencv-python>=4.1.2
-Pillow>=7.1.2
+Pillow==9.5.0
 PyYAML>=5.3.1
 requests>=2.23.0
 scipy>=1.4.1
@@ -12,6 +12,7 @@ scipy>=1.4.1
 #torchvision>=0.8.1
 tqdm>=4.41.0
 
+
 # Logging -------------------------------------
 tensorboard>=2.4.1
 # wandb