singa-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From GitBox <...@apache.org>
Subject [GitHub] [singa] XJDKC commented on issue #696: Refactor autograd module
Date Thu, 14 May 2020 11:05:07 GMT

XJDKC commented on issue #696:
URL: https://github.com/apache/singa/issues/696#issuecomment-628560755


   ```Python
   class Module:
       def compile(self, inputs, is_train, use_graph, graph_alg):
           set train, graph etc config
           ===turn on graph===
           if inputs are not filled, print warnings and fill inputs according to data type.
           self.forward(*inputs)
           ===turn off graph===
       
        def load(self, ckp_path, include_state=False):
          load onnx model and copy the params to each layer; 
          generate warnings for mismatched layers/params.
          restore the states and return it as a dict
        
        def save(self, ckp_path, state={}):
          save the model as onnx format
          save the states
       
        def forward(self, x):    # turn on graph if necessary
           pass
   
        def train_one_batch(self, x, y):  # turn on graph if necessary
           pass   
      
        @deprecated 
        def loss(self, ):
           pass
   
         @deprecated 
         def optim(self,):
             pass      
   
   
   class Layer:
       def __init__(name=None):
         self.init = False
   
       def do_init(x):
           ===turn off graph===
              init layer states
              As the graph is turned off, the initialized operations will be executed
           ===restore the state of the graph===
         
       def forward():
           # do the forward propagation 
   
       def __call__(self, x):
          if self.init == False:
             self.do_init(x)
          self.forward(x)
   
   class MyLayer(Layer):
        def __init__(self):
             self.layer1 = layer.Conv2d(nb_kernels = 32, kernel=3, stride=1, padding=0, kernel_init='he_uniform')

             self.layer2 = layer.MaxPool2d(kernel=3, stride=2)
   
         def forward(self, x):
             return self.layer2(self.layer1(x))
   
   
   
   class MyModule(Module):
        def __init__(self):
              self.blk1 = MyLayer()
              self.blk2 = MyLayer()
              self.optim = SGD()
              self.loss = CrossEntropyLoss()
   
         def forward(self, x):
              return self.blk2(self.blk1(x))    
   
         def train_one_batch(self, x, y): 
              y_ = self.forward(x)
              l = self.loss(y_, y)
              self.optim.backward_and_update(l)
              return l
   
   x = Placeholder((2, 3), device = gpu, dtype=singa.float) # alias of Tensor
   #  === no need to fill x with values===
   m = MyModel()
   
   # compatible with existing code which does not have the following two statements.
   m.compile([x], is_train=True, use_graph=True, graph_alg='sequence')
   for pname, ptensor in m.get_params():
       ptensor.uniform(-1, 1)   # not necessary if each layer's param init methods are configured.
   
   y = Placeholder((2,), device = gpu)
   for npx, npy in data:
      x.copy_from(npx)
      y.copy_from(npy)
      m.train_one_batch(x, y)  # build the graph in the first iter.  For the old code, the
params are initialized here.
   
   m.save('mymodel', state={'epoch': data.size(), 'sgd': m.optim}
   ```
   
   How about this proposal?


----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

For queries about this service, please contact Infrastructure at:
users@infra.apache.org



Mime
View raw message