DeepPoly ReLU and FullyConnected

code/deeppoly.py

@@ -44,7 +44,7 @@ def compute_verify_result(self, true_label):
 
         return weights
 
-################################################################## to understand
+    # TODO: implement Conv in resolve
     def resolve(self, constrains, layer, lower=True):
         """
         lower = True: return the lower bound
@@ -82,7 +82,7 @@ def __init__(self, in_features):
         super(DPReLU, self).__init__()
         self.in_features = in_features
         self.out_features = in_features
-        self.alpha = torch.nn.Parameter(torch.ones(in_features) * 0.3)
+        self.alpha = torch.nn.Parameter(torch.ones(in_features))
         self.alpha.requires_grad = True
 
     def forward(self, x):
@@ -151,5 +151,25 @@ def forward(self, x):
 
 
 class DPConv(nn.Module):
-    def  __init__(self, nested: nn.Conv2d):
+    def  __init__(self, nested: nn.Conv2d, in_feature):
         super(DPConv, self).__init__()
+        self.weight = nested.weight.detach()
+        self.bias = nested.bias.detach()
+        self.in_channels = nested.in_channels
+        self.out_channels = nested.out_channels
+        self.kernel_size = nested.kernel_size
+        self.stride = nested.stride
+        self.padding = nested.padding
+        self.padding_mode = nested.padding_mode
+        self.dilation = nested.dilation
+        self.groups = nested.groups
+        self.in_feature = in_feature
+        self.out_feature = func(self.in_feature)
+
+    def forward(self, x):
+        x.save()
+
+    def feature_size(self):
+        self.in_feature
+
+

code/evaluate

@@ -1,13 +1,13 @@
 #!/bin/bash
 
-rm ../examples/res.txt
+rm $1/res.txt
 for net in {1..3}
 do
 	echo Evaluating network net${net}...
-	for spec in `ls ../examples/net${net}/`
+	for spec in `ls $1/net${net}/`
 	do
 		echo ${spec}
-		res=$(python verifier.py --net net${net} --spec ../examples/net${net}/${spec})
-		echo net${k}_${net},$spec,$res >> ../examples/res.txt
+		res=$(python verifier.py --net net${net} --spec $1/net${net}/${spec})
+		echo net${k}_${net},$spec,$res >> $1/res.txt
 	done
 done

project_log

@@ -5,14 +5,15 @@
 
 
 已完成：
-
+实现给ReLU加上DeepPoly Relaxation的函数（类）
+在resolve中实现Linear的BackSub
+完成FullyConnected的测试
+在verifier.py中使用可验证的网络验证各网络
 
 ------------------------------------------------
 要做的：
-在deeppoly.py中实现给ReLU加上DeepPoly的函数（类）
-    - linear和conv的神经元也需要加上DeepPoly吗？
+在resolve中实现Conv和Res的BackSub
+DPConv的out_feature的计算
 在verifier.py中使用上述函数（类）将各网络转化为可验证的网络
-    - FullyConnected
     - Conv
-    - NormalizedResnet
-在verifier.py中使用可验证的网络验证各网络
+    - NormalizedResnet