I checked the name, and created a new state_dict dictionary and inserted the 112 keys that were in both checkpoint and model using the snippet below :
new_state_dict  = {}
checkpoint_state_dict = torch.load(checkpoint_path, map_location=lambda storage, loc: storage) 
for (ck, cp) in checkpoint_state_dict.items():
    for (mk, mp) in model.state_dict().items():
        kname,kext = os.path.splitext(ck)
        mname,mext = os.path.splitext(mk)
        # check the two parameter and see if they are the same
        # then use models key naming scheme and use checkpoints weights
        if kname+kext == mname+mext or kname+'.0'+kext == mname+mext:
            new_state_dict[mname+mext] = cp 
        else: 
             if kext in ('.scale','.zero_point'):
                 new_state_dict[ck] = cp


and then use  this new state_dict! yet I’m getting  the ver same exact errors!
meaning errors like  this :
RuntimeError: Error(s) in loading state_dict for RetinaFace:
        While copying the parameter named "ssh1.conv3X3.0.weight", whose dimensions in the model are torch.Size([32, 64, 3, 3]) and whose dimensions in the checkpoint are torch.Size([32, 64, 3, 3]).

This is really frustrating and there is no documentation concerning this! I’m completely clueless here.