nudles commented on issue #691:
URL: https://github.com/apache/singa/issues/691#issuecomment-629269482
**Updated on May 15 Night**
```python
class Layer:
def get_params(self):
"""the params of this layer and sublayers as a dict; param name is:
layername.param
e.g., self.W = Tensor(), self.b=Tensor()
name of W and b is like conv1.W and conv1.b
"""
def get_states(self):
"""states of this layer as sublayers that are necessary for model
training/evaluation/inference.
the states include the params and others, e.g., the running mean
and var of batchnorm.
"""
class Module(Layer):
def compile(self ...):
"""set the name of each layer and sublayers, which will be used to
create the dict
for get_params and get_states. Then no need to manually config the
layer name
the __init__ method of a layer.
For instance,
class Blk(Layer):
def __init__(self):
self.conv1= Conv2d()
self.conv2 = Conv2d()
class MyModel(Module):
def __init__(self):
self.blk1 = Blk() --> blk1.conv1, blk1.conv2
self.blk2 = Blk() --> blk2.conv1, blk2.conv2
"""
# high priority
def save_states(self, fpath, aux_states={}):
"""Save states.
Args:
fpath: output file path (without the extension)
aux_states(dict): values are standard data types or Tensor,
e.g., epoch ID, learning rate, optimizer
states
"""
states = get_states() + aux_states + input_placeholders
tensor_dict = {}
for k, v in states:
if type(v) is Tensor:
tensor_dict[k] = v
states[k] = {'shape': v.shape, 'dtype': v.dtype}
save states as json file
save tensor_dict via numpy or hdf5 or protobuf
zip the output files
def load_states(self, fpath, dev, use_graph=True, graph_alg='sequence'):
"""Load the model onto dev
Args:
path: input file path (without the extension)
Returns:
dict
```
unzip the input file
load the json file --> states
load the tensor files --> tensor_dict
put the tensors into states
states --> model_states + input_placeholders + aux_states
self.compile(input_placeholders, dev, use_graph, graph_alg)
model.set_states(model_states)
return the rest states as a dict
# lower priority
def save(fpath, model):
attributes <-- model
replace all tensors in attributes --> {'shape': v.shape, 'dtype':
v.dtype}
dump the tensors via numpy or protobuf or hdf5
dump model via pickle
zip the output files
def load(fpath, dev, use_graph, graph_alg):
unzip the input file
load model via pickle
load tensors
restore the tensors in model attributes
return the model
# handle ONNX
def to_onnx(model):
return a onnx model
class SONNXModel(Module):
def __init__(self, onnx_model):
self.store_output = store_output
for layer_name, layer_config in get_layer(onnx_model):
self.__dict__[layer_name] = CreateLayer(...)
def forward(self, aux_output):
run forward according to onnx graph
return the last output + aux_output
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)
....
```
Clarification:
* Params: layer parameters (Tensor) that are updated via SGD.
`Layer.get_params()`
* States: Params + other variables that are necessary for model
evaluation/inference. Superset of params. `Layer.get_states()`
* Attributes: members of a class instance `class.__dict__`. Superset of
states.
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