add the dropout_p parameter (#10805)

models/common.py

@@ -846,12 +846,19 @@ class Proto(nn.Module):
 
 class Classify(nn.Module):
     # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
+    def __init__(self,
+                 c1,
+                 c2,
+                 k=1,
+                 s=1,
+                 p=None,
+                 g=1,
+                 dropout_p=0.0):  # ch_in, ch_out, kernel, stride, padding, groups, dropout probability
         super().__init__()
         c_ = 1280  # efficientnet_b0 size
         self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
         self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
-        self.drop = nn.Dropout(p=0.0, inplace=True)
+        self.drop = nn.Dropout(p=dropout_p, inplace=True)
         self.linear = nn.Linear(c_, c2)  # to x(b,c2)
 
     def forward(self, x):