Bottleneck.patch

--- /home/asmita/Work/fine-tuning-pytorch/networks/resnet.py
+++ /home/asmita/Work/fine-tuning-pytorch/networks/resnet.py
@@ -4,15 +4,15 @@
     def __init__(self, inplanes, planes, stride=1, downsample=None):
         super(Bottleneck, self).__init__()
         self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        # self.bn1 = nn.BatchNorm2d(planes)
-        self.bn1 = MyBatchNorm2d(planes)
+        self.bn1 = nn.BatchNorm2d(planes)
+        # self.bn1 = MyBatchNorm2d(planes)
         self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                padding=1, bias=False)
-        # self.bn2 = nn.BatchNorm2d(planes)
-        self.bn2 = MyBatchNorm2d(planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        # self.bn2 = MyBatchNorm2d(planes)
         self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
-        # self.bn3 = nn.BatchNorm2d(planes * 4)
-        self.bn3 = MyBatchNorm2d(planes * 4)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        # self.bn3 = MyBatchNorm2d(planes * 4)
         self.relu = nn.ReLU(inplace=True)
         self.downsample = downsample
         self.stride = stride