Update hello-world.py

examples/hello-world.py

@@ -5,10 +5,11 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
+import torch.legacy.nn as nn
 import sparseconvnet.legacy as scn
 
 # Use the GPU if there is one, otherwise CPU
-tensorType = 'torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatTensor'
+dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatTensor'
 
 model = scn.Sequential().add(
     scn.SparseVggNet(2, 1,
@@ -21,7 +22,7 @@ model = scn.Sequential().add(
     scn.BatchNormReLU(32)
 ).add(
     scn.SparseToDense(2)
-).type(tensorType)
+).type(dtype)
 
 # output will be 10x10
 inputSpatialSize = model.suggestInputSize(torch.LongTensor([10, 10]))
@@ -50,7 +51,7 @@ for y, line in enumerate(msg):
 input.precomputeMetadata(3)
 
 model.evaluate()
-input.type(tensorType)
+input.type(dtype)
 output = model.forward(input)
 
 # Output is 1x32x10x10: our minibatch has 1 sample, the network has 32 output