sandeep-krishnamurthy commented on a change in pull request #12412: Infer dtype
in SymbolBlock import from input symbol
URL: https://github.com/apache/incubator-mxnet/pull/12412#discussion_r218573269
##########
File path: python/mxnet/gluon/block.py
##########
@@ -1084,5 +1092,71 @@ def _clear_cached_op(self):
super(SymbolBlock, self)._clear_cached_op()
self._cached_graph = tmp
+ def cast(self, dtype):
+ self._clear_cached_op()
+ super(SymbolBlock, self).cast(dtype)
+
def hybrid_forward(self, F, x, *args, **kwargs):
raise NotImplementedError
+
+def _infer_param_types(in_params, out_params, arg_params, aux_params,
default_dtype=mx_real_t):
+ """Utility function that helps in inferring DType of args and auxs params
+ from given input param.
+
+ Parameters
+ ----------
+ in_params: List of Symbol
+ List of input symbol variables.
+ out_params: Symbol
+ Output symbol variable.
+ arg_params: List of Str
+ List of names of argument parametrs.
+ aux_params: List of Str
+ List of names of auxiliary parameters.
+ default_dtype: numpy.dtype or str, default 'float32'
+ Default data type for arg_params and aux_params, if unable to infer
the type.
+
+ Returns
+ -------
+ arg_types: List of numpy.dtype
+ List of arg_params type. Order is same as arg_params.
+ Defaults to 'float32', if unable to infer type.
+ aux_types: List of numpy.dtype
+ List of aux_params type. Order is same as aux_params.
+ Defaults to 'float32', if unable to infer type.
+ """
+ arg_types = None
+ aux_types = None
+
+ # Get Input symbol details. This will be used to infer types of
+ # other parameters.
+ input_sym_names = [in_param.name for in_param in in_params]
+
+ # Try to infer input types. If not successful, we will set default dtype.
Review comment:
In one of the unit test(pasting below) infer shape will be false and it need
not be an issue, users are just assuming default type.
```python
model = nn.HybridSequential()
model.add(nn.Dense(128, activation='tanh'))
model.add(nn.Dropout(0.5))
model.add(nn.Dense(64, activation='tanh'),
nn.Dense(32, in_units=64))
model.add(nn.Activation('relu'))
model.initialize()
inputs = mx.sym.var('data')
outputs = model(inputs).get_internals()
smodel = gluon.SymbolBlock(outputs, inputs,
params=model.collect_params())
```
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