Tutorial 8: Apply existing algorithms to new tasks

Here we show how to apply existing algorithms to other existing tasks with an example of SPOS & DetNAS.

1. Register a new algorithm for the other existing task with an existing algorithm

   Create a new file mmrazor/models/algorithms/detnas.py, class DetNAS inherits from class SPOS

   from mmrazor.models.builder import ALGORITHMS
   from .spos import SPOS
   
   @ALGORITHMS.register_module()
   class DetNAS(SPOS):
   
       def __init__(self, **kwargs):
           super(DetNAS, self).__init__(**kwargs)
   

2. Add your custom functions (optional)

   If you need other custom functions according to the other existing task, you can add them in class DetNAS as follows.

   class DetNAS(SPOS):
   
       ...
   
       def _init_flops(self):
           flops_model = copy.deepcopy(self.architecture)
           flops_model = revert_sync_batchnorm(flops_model)
           flops_model.eval()
           flops, params = get_model_complexity_info(flops_model.model.backbone,
                                                   self.input_shape)
           flops_lookup = dict()
           for name, module in flops_model.named_modules():
               flops = getattr(module, '__flops__', 0)
               flops_lookup[name] = flops
           del (flops_model)
   
           for name, module in self.architecture.named_modules():
               module.__flops__ = flops_lookup[name]
   
       ...
   
   

3. Import the class

   You can either add the following line to mmrazor/models/algorithms/__init__.py

   from .detnas import DetNAS
   

   or alternatively add

   custom_imports = dict(
       imports=['mmrazor.models.algorithms.detnas'],
       allow_failed_imports=False)
   

   to the config file to avoid modifying the original code.

4. Use the algorithm in your config file

   algorithm = dict(
       type='DetNAS',
       ...
   )