Create CUSTOMRESNET.py

CUSTOMRESNET.py

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+import torch.nn.functional as F
+import torch
+import torch.nn as nn
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        #drop=0.01
+        # Preparation Layer
+        self.conv1 = nn.Sequential (
+            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1,bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True)
+            )  # Number of Parameters = 3*3*3*64=1728
+        # Layer 1
+        self.conv11 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1,bias=False),
+            nn.MaxPool2d(kernel_size=2,stride=2),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True)
+            )  # Number of Parameters = 3*3*64*128 = 73728
+        self.conv12 = nn.Sequential(
+            nn.Conv2d(128,128, kernel_size=3, stride=1, padding=1,bias=False),# Number of Parameters = 3*3*64*128 = 73728
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128,128, kernel_size=3, stride=1, padding=1,bias=False),# Number of Parameters = 3*3*64*128 = 73728
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True)
+            )
+
+        # Layer 2
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1,bias=False),
+            nn.MaxPool2d(kernel_size=2,stride=2),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True)
+            )
+
+        # Layer 3
+        self.conv31 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1,bias=False),
+            nn.MaxPool2d(kernel_size=2,stride=2),
+            nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True)
+            )
+        self.conv32 = nn.Sequential(
+            nn.Conv2d(512,512, kernel_size=3, stride=1, padding=1,bias=False),
+            nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512,512, kernel_size=3, stride=1, padding=1,bias=False),
+            nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True)
+            )
+
+        self.maxpool = nn.MaxPool2d(kernel_size=4,stride=2)
+
+        # Fully connected
+        self.fc = nn.Linear(512, 10, bias=True)
+
+
+    def forward(self, x):
+        x = self.conv1(x)
+
+        x = self.conv11(x)
+        R1=x
+        x = self.conv12(x)
+        x=x+R1
+
+        x = self.conv2(x)
+
+        x = self.conv31(x)
+        R2=x
+        x = self.conv32(x)
+        x=x+R2
+
+        x = self.maxpool(x)
+        #x = x.randn(512, 1)
+
+# squeeze the tensor to size 512x
+        x = x.squeeze(dim=[2, 3])
+
+        #x = x.view(512, 10)
+
+        x = self.fc(x)
+
+        x = x.view(-1, 10)
+        return x
+        #y = F.log_softmax(x, dim=-1)
+        #return  y
+
+def model_summary(model,input_size):
+    model = Net().to(device)
+    summary(model, input_size=(3, 32, 32))
+    return model,input_size