add FRFBSSD

Author: lzx1413

models/base_models.py

@@ -35,3 +35,72 @@ def vgg(cfg, i, batch_norm=False):
     '512': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
             512, 512, 512],
 }
+class BasicConv(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
+        super(BasicConv, self).__init__()
+        self.out_channels = out_planes
+        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
+        self.bn = nn.BatchNorm2d(out_planes,eps=1e-5, momentum=0.01, affine=True) if bn else None
+        self.relu = nn.ReLU(inplace=True) if relu else None
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn is not None:
+            x = self.bn(x)
+        if self.relu is not None:
+            x = self.relu(x)
+        return x
+class BasicRFB_a(nn.Module):
+
+    def __init__(self, in_planes, out_planes, stride=1, scale = 0.1):
+        super(BasicRFB_a, self).__init__()
+        self.scale = scale
+        self.out_channels = out_planes
+        inter_planes = in_planes //4
+
+
+        self.branch0 = nn.Sequential(
+            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
+            BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=1,relu=False)
+        )
+        self.branch1 = nn.Sequential(
+            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
+            BasicConv(inter_planes, inter_planes, kernel_size=(3,1), stride=1, padding=(1,0)),
+            BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3, relu=False)
+        )
+        self.branch2 = nn.Sequential(
+            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
+            BasicConv(inter_planes, inter_planes, kernel_size=(1,3), stride=stride, padding=(0,1)),
+            BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3, relu=False)
+        )
+        '''
+        self.branch3 = nn.Sequential(
+                BasicConv(in_planes, inter_planes, kernel_size=1, stride=1),
+                BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=1),
+                BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=3, dilation=3, relu=False)
+                )
+        '''
+        self.branch3 = nn.Sequential(
+            BasicConv(in_planes, inter_planes//2, kernel_size=1, stride=1),
+            BasicConv(inter_planes//2, (inter_planes//4)*3, kernel_size=(1,3), stride=1, padding=(0,1)),
+            BasicConv((inter_planes//4)*3, inter_planes, kernel_size=(3,1), stride=stride, padding=(1,0)),
+            BasicConv(inter_planes, inter_planes, kernel_size=3, stride=1, padding=5, dilation=5, relu=False)
+        )
+
+        self.ConvLinear = BasicConv(4*inter_planes, out_planes, kernel_size=1, stride=1, relu=False)
+        self.shortcut = BasicConv(in_planes, out_planes, kernel_size=1, stride=stride, relu=False)
+        self.relu = nn.ReLU(inplace=False)
+    def forward(self,x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+
+        out = torch.cat((x0,x1,x2,x3),1)
+        out = self.ConvLinear(out)
+        short = self.shortcut(x)
+        out = out*self.scale + short
+        out = self.relu(out)
+
+        return out