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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