models.py

from imports import *

class StdConv(nn.Module):
    def __init__(self, nin, nout, stride=2, drop=0.1):
        super().__init__()
        self.conv = nn.Conv2d(nin, nout, 3, stride=stride, padding=1)
        self.bn = nn.BatchNorm2d(nout)
        self.drop = nn.Dropout(drop)
        
    def forward(self, x): return self.drop(self.bn(F.relu(self.conv(x))))


def flatten_conv(x,k):
    bs,nf,gx,gy = x.size()
    x = x.permute(0,2,3,1).contiguous()
    return x.view(bs,-1,nf//k)


class OutConv(nn.Module):
    def __init__(self, k, nin, bias):
        super().__init__()
        self.k = k
        self.oconv1 = nn.Conv2d(nin, (len(id2cat)+1)*k, 3, padding=1)
        self.oconv2 = nn.Conv2d(nin, 4*k, 3, padding=1)
        self.oconv1.bias.data.zero_().add_(bias)
        
    def forward(self, x):
        return [flatten_conv(self.oconv1(x), self.k),
                flatten_conv(self.oconv2(x), self.k)]


drop=0.4

class SSD_MultiHead(nn.Module):
    def __init__(self, k, bias):
        super().__init__()
        self.drop = nn.Dropout(drop)
        self.sconv0 = StdConv(512,256, stride=1, drop=drop)
        self.sconv1 = StdConv(256,256, drop=drop)
        self.sconv2 = StdConv(256,256, drop=drop)
        self.sconv3 = StdConv(256,256, drop=drop)
        self.out0 = OutConv(k, 256, bias)
        self.out1 = OutConv(k, 256, bias)
        self.out2 = OutConv(k, 256, bias)
        self.out3 = OutConv(k, 256, bias)

    def forward(self, x):
        x = self.drop(F.relu(x))
        x = self.sconv0(x)
        x = self.sconv1(x)
        o1c,o1l = self.out1(x)
        x = self.sconv2(x)
        o2c,o2l = self.out2(x)
        x = self.sconv3(x)
        o3c,o3l = self.out3(x)
        return [torch.cat([o1c,o2c,o3c], dim=1),
                torch.cat([o1l,o2l,o3l], dim=1)]