dcslin edited a comment on issue #696:
URL: https://github.com/apache/singa/issues/696#issuecomment-628355516
Considering the API requirement, and constraints as below:
API requirement:
1. [**Model**] multi-input/output (multi loss fn)
2. [**Model**] Load model from disk, in other words: Model param memory
allocation should be done in `model.__init__`
API constraints:
1. [**Model**] graph module buffer first forward call **or** turn off graph
module in the first forward call
2. [**Layer**] layer param memory allocation & initialization requires input
x
@XJDKC 's scheme 2 and @nudles 's `Placeholder` is close to current
implemenation, and changes required could be small.
For model building:
```python
class MyModel(Model):
def __init__(self, inputs, configs):
self.mylayer=MyLayer(configs)
self.linear1=Linear(configs, kernel_init=configs.ker_init)
super.__init__(inputs) # maybe a bit confuse for user what is this
def forward(self, inputs):
return linear1(mylayer(inputs[0], inputs[1]))
```
For model running:
```python
x=PlaceHolder(shape=(batch, shape1, shape2))
m=MyModel([x],**configs,**graph_configs)
m.on_device(gpu)
m.train()
for e in epochs:
for x, y in data_gen:
losses = m.loss(y, m(x))
m.optim(l1)
m.optim(l2)
```
For Layer building:
```python
class MyLayer(Layer):
def __init__(self, configs):
self.configs = configs
def __call__(self, inputs):
if not self.init:
self.W = Tensor(self.configs, inputs.shape).initializer()
self.device_check(inputs, self.W)
self.init=True
return = op1(inputs[0], inputs[1])
```
For Module class impl:
```python
class Module:
def __init__(self, placeholder_input, configs):
turn_off_graph()
self.forward(*placeholder_input)
turn_on_graph()
def __call__(self,inputs):
return self.forward(*inputs)
```
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