Cherry pick revert RN quantization changes.

src/sparseml/pytorch/models/classification/resnet.py

@@ -185,7 +185,11 @@ def __init__(self, in_channels: int, out_channels: int, stride: int = 1):
             else None
         )
 
-        self.add_relu = _AddReLU(out_channels)
+        # self.add_relu = _AddReLU(out_channels)
+        if FloatFunctional:
+            self.add_relu = FloatFunctional()
+        else:
+            self.add_relu = ReLU(num_channels=out_channels, inplace=True)
 
         self.initialize()
 
@@ -198,9 +202,13 @@ def forward(self, inp: Tensor):
         out = self.bn2(out)
 
         identity_val = self.identity(inp) if self.identity is not None else inp
-        out = self.add_relu(identity_val, out)
+        # out = self.add_relu(identity_val, out)
+        # return out
 
-        return out
+        if isinstance(self.add_relu, FloatFunctional):
+            return self.add_relu.add_relu(identity_val, out)
+        else:
+            return self.add_relu(identity_val + out)
 
     def initialize(self):
         _init_conv(self.conv1)
@@ -242,7 +250,11 @@ def __init__(
             else None
         )
 
-        self.add_relu = _AddReLU(out_channels)
+        # self.add_relu = _AddReLU(out_channels)
+        if FloatFunctional:
+            self.add_relu = FloatFunctional()
+        else:
+            self.add_relu = ReLU(num_channels=out_channels, inplace=True)
 
         self.initialize()
 
@@ -260,9 +272,13 @@ def forward(self, inp: Tensor):
 
         identity_val = self.identity(inp) if self.identity is not None else inp
 
-        out = self.add_relu(identity_val, out)
+        # out = self.add_relu(identity_val, out)
+        # return out
 
-        return out
+        if isinstance(self.add_relu, FloatFunctional):
+            return self.add_relu.add_relu(identity_val, out)
+        else:
+            return self.add_relu(identity_val + out)
 
     def initialize(self):
         _init_conv(self.conv1)