joddiy commented on issue #691:
URL: https://github.com/apache/singa/issues/691#issuecomment-629608607
```
# handle ONNX
def to_onnx(model):
return a onnx model
class SONNXModel(Module):
def __init__(self, onnx_mode):
singa_rep = sonnx.prepare(onnx_model) # will update the prepare
function to remove device and batchsize
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, inputs, is_train, use_graph, graph_alg)
# init weights
super.compile(self, inputs, is_train, use_graph, graph_alg)
# 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]) ** remember to
release self.weights to free memory.
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)
....
ox = onnx.load(fpath)
x = Placeholder((2, 3), device = gpu, dtype=singa.float) # alias of Tensor
m = MyModel(ox)
# compatible with existing code which does not have the following two
statements.
m.compile([x], is_train=True, use_graph=True, graph_alg='sequence')
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.
```
update code with the comments with `**`
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