Remove Variable

Author: kuangliu

models/densenet.py

@@ -5,8 +5,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class Bottleneck(nn.Module):
     def __init__(self, in_planes, growth_rate):
@@ -100,10 +98,10 @@ def DenseNet161():
 def densenet_cifar():
     return DenseNet(Bottleneck, [6,12,24,16], growth_rate=12)
 
-def test_densenet():
+def test():
     net = densenet_cifar()
     x = torch.randn(1,3,32,32)
-    y = net(Variable(x))
+    y = net(x)
     print(y)
 
-# test_densenet()
+# test()

models/dpn.py

@@ -3,8 +3,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class Bottleneck(nn.Module):
     def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
@@ -93,7 +91,7 @@ def DPN92():
 
 def test():
     net = DPN92()
-    x = Variable(torch.randn(1,3,32,32))
+    x = torch.randn(1,3,32,32)
     y = net(x)
     print(y)
 

models/googlenet.py

@@ -3,8 +3,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class Inception(nn.Module):
     def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
@@ -99,7 +97,11 @@ def forward(self, x):
         out = self.linear(out)
         return out
 
-# net = GoogLeNet()
-# x = torch.randn(1,3,32,32)
-# y = net(Variable(x))
-# print(y.size())
+
+def test():
+    net = GoogLeNet()
+    x = torch.randn(1,3,32,32)
+    y = net(x)
+    print(y.size())
+
+# test()

models/mobilenet.py

@@ -7,8 +7,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class Block(nn.Module):
     '''Depthwise conv + Pointwise conv'''
@@ -57,7 +55,7 @@ def forward(self, x):
 def test():
     net = MobileNet()
     x = torch.randn(1,3,32,32)
-    y = net(Variable(x))
+    y = net(x)
     print(y.size())
 
 # test()

models/mobilenetv2.py

@@ -7,8 +7,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class Block(nn.Module):
     '''expand + depthwise + pointwise'''
@@ -81,7 +79,7 @@ def forward(self, x):
 
 def test():
     net = MobileNetV2()
-    x = Variable(torch.randn(2,3,32,32))
+    x = torch.randn(2,3,32,32)
     y = net(x)
     print(y.size())
 

models/pnasnet.py

@@ -6,8 +6,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class SepConv(nn.Module):
     '''Separable Convolution.'''
@@ -120,8 +118,7 @@ def PNASNetB():
 
 def test():
     net = PNASNetB()
-    print(net)
-    x = Variable(torch.randn(1,3,32,32))
+    x = torch.randn(1,3,32,32)
     y = net(x)
     print(y)
 

models/preact_resnet.py

@@ -8,8 +8,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class PreActBlock(nn.Module):
     '''Pre-activation version of the BasicBlock.'''
@@ -114,7 +112,7 @@ def PreActResNet152():
 
 def test():
     net = PreActResNet18()
-    y = net(Variable(torch.randn(1,3,32,32)))
+    y = net((torch.randn(1,3,32,32))
     print(y.size())
 
 # test()

models/resnet.py

@@ -10,8 +10,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class BasicBlock(nn.Module):
     expansion = 1
@@ -117,7 +115,7 @@ def ResNet152():
 
 def test():
     net = ResNet18()
-    y = net(Variable(torch.randn(1,3,32,32)))
+    y = net(torch.randn(1,3,32,32))
     print(y.size())
 
 # test()

models/resnext.py

@@ -6,8 +6,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class Block(nn.Module):
     '''Grouped convolution block.'''
@@ -91,7 +89,7 @@ def ResNeXt29_32x4d():
 def test_resnext():
     net = ResNeXt29_2x64d()
     x = torch.randn(1,3,32,32)
-    y = net(Variable(x))
+    y = net(x)
     print(y.size())
 
 # test_resnext()

models/senet.py

@@ -6,8 +6,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from torch.autograd import Variable
-
 
 class BasicBlock(nn.Module):
     def __init__(self, in_planes, planes, stride=1):
@@ -117,7 +115,7 @@ def SENet18():
 
 def test():
     net = SENet18()
-    y = net(Variable(torch.randn(1,3,32,32)))
+    y = net(torch.randn(1,3,32,32))
     print(y.size())
 
 # test()