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From GitBox <...@apache.org>
Subject [GitHub] [singa] joddiy edited a comment on issue #691: Add save and load method for Module class
Date Sat, 16 May 2020 07:52:48 GMT

joddiy edited a comment on issue #691:
URL: https://github.com/apache/singa/issues/691#issuecomment-629604531


   ```
   # handle ONNX 
   def to_onnx(model):
       return a onnx model 
   
   class SONNXModel(Module):
       def __init__(self, onnx_model):
           singa_rep = sonnx.prepare(onnx_model, device=dev, batchsize=1)
           for layer_name, layer in singa_rep.layers:
           self.__dict__[layer_name] = layer
           # store weights here as numpy
           for weith_name, weight in singa_rep.weights:
               self.weights[weith_name] = weight
           # store layer info such as input and output name(only weights)
           for layer_name, layer_info in singa_rep.layer_infos:
               self.layer_infos[layer_name] = layer_info
   
       def forward(self, aux_output):
           # run forward according to onnx graph 
           return the last output + aux_output
   
       def compile(self)
           # init weights
           super.compile(self)
           # set weights' value
       for layer_name, layer in self.__dict__:
           input_info, output_info = self.layer_infos[layer_name]
           for input_name in input_info:
               layer.set_weight(self.weights[input_name])
   
   class MyModel(SONNXModel):
        def __init__(self, onnx):
             super.__init__(onnx)
             self.layer1 = Conv()
             self.layer2 = Conv()
   
        def forward(self, x):
              x1, x2 = super.forward(x, aux_output)
              x = self.layer1.forward(x2)
              return self.layer2.forward(x1) + x
   
         def train_one_batch(self, x, y):
              y_ = self.forward(x)
              ....
   ```
   How about this one, we pareses onnx by `soon.prepare`(Backend), it returns a `singa_rep`(BackendRep),
and the  singa_rep contains the layers, weights and input_output_info, we store the layers
in self.__dict__. When we compile the model, first we call super() to init the params, then
we set its value from the onnx loaded weights.


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