diff --git a/models/efficientnet.py b/models/efficientnet.py
index 9f8f976ab8..891add39b4 100644
--- a/models/efficientnet.py
+++ b/models/efficientnet.py
@@ -44,8 +44,8 @@ class MBConvBlock:
     # has_se
     if self.has_se:
       x_squeezed = x.avg_pool2d(kernel_size=x.shape[2:4])
-      x_squeezed = x_squeezed.conv2d(self._se_reduce).add(self._se_reduce_bias.reshape(shape=[1, -1, 1, 1])).swish()
-      x_squeezed = x_squeezed.conv2d(self._se_expand).add(self._se_expand_bias.reshape(shape=[1, -1, 1, 1]))
+      x_squeezed = x_squeezed.conv2d(self._se_reduce, self._se_reduce_bias).swish()
+      x_squeezed = x_squeezed.conv2d(self._se_expand, self._se_expand_bias)
       x = x.mul(x_squeezed.sigmoid())
 
     x = self._bn2(x.conv2d(self._project_conv))
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 062f13de6e..eda8b5b4d4 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -308,6 +308,10 @@ class Tensor:
   def max_pool2d(self, kernel_size=(2,2)):
     return self._pool2d(*kernel_size).max(axis=(3,5))
 
+  def conv2d(self, weight, bias=None, stride=1, groups=1):
+    ret = self._conv2d(weight, stride=stride, groups=groups)
+    return ret if bias is None else ret.add(bias.reshape(shape=[1, -1, 1, 1]))
+
   # ***** functional nn ops *****
 
   def linear(self, weight, bias):