diff --git a/examples/efficientnet.py b/examples/efficientnet.py
index 7e8afbb5b3..dd1d015ab4 100644
--- a/examples/efficientnet.py
+++ b/examples/efficientnet.py
@@ -3,7 +3,6 @@
 # a rough copy of
 # https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py
 import os
-GPU = os.getenv("GPU", None) is not None
 import sys
 import io
 import time
@@ -37,10 +36,7 @@ def infer(model, img):
   img /= np.array([0.229, 0.224, 0.225]).reshape((1,-1,1,1))
 
   # run the net
-  if GPU:
-    out = model.forward(Tensor(img).gpu()).cpu()
-  else:
-    out = model.forward(Tensor(img))
+  out = model.forward(Tensor(img)).cpu()
 
   # if you want to look at the outputs
   """
@@ -54,8 +50,6 @@ if __name__ == "__main__":
   # instantiate my net
   model = EfficientNet(int(os.getenv("NUM", "0")))
   model.load_weights_from_torch()
-  if GPU:
-    [x.gpu_() for x in get_parameters(model)]
 
   # category labels
   import ast
diff --git a/extra/efficientnet.py b/extra/efficientnet.py
index 7af1243f09..9e60ca1944 100644
--- a/extra/efficientnet.py
+++ b/extra/efficientnet.py
@@ -159,10 +159,11 @@ class EfficientNet:
           mv = eval(mk.replace(".weight", ""))
         except AttributeError:
           mv = eval(mk.replace(".bias", "_bias"))
-      vnp = v.numpy().astype(np.float32) if USE_TORCH else v
+      vnp = v.numpy().astype(np.float32) if USE_TORCH else v.astype(np.float32)
       vnp = vnp if k != '_fc.weight' else vnp.T
+      vnp = vnp if vnp.shape != () else np.array([vnp])
 
-      if mv.shape == vnp.shape or vnp.shape == ():
-        mv.data[:] = vnp
+      if mv.shape == vnp.shape:
+        mv.assign(Tensor(vnp))
       else:
         print("MISMATCH SHAPE IN %s, %r %r" % (k, mv.shape, vnp.shape))