fix models for PyTorch v0.4 (remove .data and add _ for the initializations …

torchvision/models/densenet.py

@@ -209,12 +209,12 @@ def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
         # Official init from torch repo.
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal(m.weight.data)
+                nn.init.kaiming_normal_(m.weight)
             elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
+                m.weight.fill_(1)
+                m.bias.zero_()
             elif isinstance(m, nn.Linear):
-                m.bias.data.zero_()
+                m.bias.zero_()
 
     def forward(self, x):
         features = self.features(x)

torchvision/models/inception.py

@@ -61,12 +61,12 @@ def __init__(self, num_classes=1000, aux_logits=True, transform_input=False):
                 import scipy.stats as stats
                 stddev = m.stddev if hasattr(m, 'stddev') else 0.1
                 X = stats.truncnorm(-2, 2, scale=stddev)
-                values = torch.Tensor(X.rvs(m.weight.data.numel()))
-                values = values.view(m.weight.data.size())
-                m.weight.data.copy_(values)
+                values = torch.Tensor(X.rvs(m.weight.numel()))
+                values = values.view(m.weight.size())
+                m.weight.copy_(values)
             elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
+                m.weight.fill_(1)
+                m.bias.zero_()
 
     def forward(self, x):
         if self.transform_input:

torchvision/models/resnet.py

@@ -113,10 +113,10 @@ def __init__(self, block, layers, num_classes=1000):
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
+                m.weight.normal_(0, math.sqrt(2. / n))
             elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
+                m.weight.fill_(1)
+                m.bias.zero_()
 
     def _make_layer(self, block, planes, blocks, stride=1):
         downsample = None

torchvision/models/squeezenet.py

@@ -89,11 +89,11 @@ def __init__(self, version=1.0, num_classes=1000):
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
                 if m is final_conv:
-                    init.normal(m.weight.data, mean=0.0, std=0.01)
+                    init.normal(m.weight, mean=0.0, std=0.01)
                 else:
-                    init.kaiming_uniform(m.weight.data)
+                    init.kaiming_uniform_(m.weight)
                 if m.bias is not None:
-                    m.bias.data.zero_()
+                    m.bias.zero_()
 
     def forward(self, x):
         x = self.features(x)

torchvision/models/vgg.py

@@ -48,15 +48,15 @@ def _initialize_weights(self):
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
+                m.weight.normal_(0, math.sqrt(2. / n))
                 if m.bias is not None:
-                    m.bias.data.zero_()
+                    m.bias.zero_()
             elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
+                m.weight.fill_(1)
+                m.bias.zero_()
             elif isinstance(m, nn.Linear):
-                m.weight.data.normal_(0, 0.01)
-                m.bias.data.zero_()
+                m.weight.normal_(0, 0.01)
+                m.bias.zero_()
 
 
 def make_layers(cfg, batch_norm=False):