Well I copied this from Dilated ResNet but the idea is the same.
1st of all since you are modifiying the architecture, when you train to load pretrained weights it will raise up an error.
def drn_c_26(pretrained=False, **kwargs):
    model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-c-26']),strict=False)
    return model

This is cos pretrained is looking for the exact architecture. when u use model.load_state_dict set strict=False
For setting both classifiers (I imagine a fully connected with softmax ), you just have to create them in the forward function.
foward(self,x):
 ...
...
x=conv2d(x)
output1 = classifier1(x)
output2 = classifier2(x)
return output1, output2

Then you just have to create a new loss which sums both classifier’s loses (in the main file) and that’s all