preliminary submission without resnet

code/deeppoly.py

@@ -323,24 +323,25 @@ def pad_image(temp_input, padding):
     return temp_input
 
 
-class DPBatchNorm2d(nn.Module):
-    def __init__(self, in_features):
-        super(DPBatchNorm2d, self).__init__()
-        self.in_features = in_features
-        self.out_features = in_features
-
-    def forward(self, x):
-        x.save()
-        low, up = x.lb, x.ub
-
-        x.lb = F.relu(low)
-        x.ub = F.relu(up)
-        x.slb = torch.cat([curr_slb.unsqueeze(0), curr_slb_bias.unsqueeze(0)], dim=0)
-        x.sub = torch.cat([curr_sub.unsqueeze(0), curr_sub_bias.unsqueeze(0)], dim=0)
-        x.is_relu = True
-        return x
-
-    @staticmethod
-    def batchNorm(x):
-        one_channel
-
+# class DPBatchNorm2d(nn.Module):
+#     def __init__(self, in_features):
+#         super(DPBatchNorm2d, self).__init__()
+#         self.in_features = in_features
+#         self.out_features = in_features
+#
+#     def forward(self, x):
+#         x.save()
+#         x.lb = self.batchNorm(x.lb)
+#         x.ub = self.batchNorm(x.ub)
+#         curr_slb =
+#         x.slb = torch.cat([curr_slb.unsqueeze(0), curr_slb_bias.unsqueeze(0)], dim=0)
+#         x.sub = torch.cat([curr_sub.unsqueeze(0), curr_sub_bias.unsqueeze(0)], dim=0)
+#         x.is_relu = True
+#         return x
+#
+#     @staticmethod
+#     def batchNorm(x):
+#         mean = torch.mean(x)
+#         std = torch.std(x, unbiased=False)
+#         one_channel_batchnorm = (x - mean) / std
+#         return one_channel_batchnorm

code/verifier.py

@@ -275,7 +275,7 @@ def main():
 
     inputs, true_label, eps = get_spec(args.spec, dataset)
     net = get_net(args.net, net_name)
-    print(net)
+    # print(net)
 
     outs = net(inputs)
     pred_label = outs.max(dim=1)[1].item()