def get_layer(hparams: Union[HParams, Dict[str, Any]]) -> nn.Module:
r"""Makes a layer instance.
The layer must be an instance of :torch_nn:`Module`.
Args:
hparams (dict or HParams): Hyperparameters of the layer, with
structure:
.. code-block:: python
{
"type": "LayerClass",
"kwargs": {
# Keyword arguments of the layer class
# ...
}
}
Here:
`"type"`: str or layer class or layer instance
The layer type. This can be
- The string name or full module path of a layer class. If
the class name is provided, the class must be in module
:torch_nn:`Module`, :mod:`texar.torch.core`, or
:mod:`texar.torch.custom`.
- A layer class.
- An instance of a layer class.
For example
.. code-block:: python
"type": "Conv1D" # class name
"type": "texar.torch.core.MaxReducePooling1D" # module path
"type": "my_module.MyLayer" # module path
"type": torch.nn.Module.Linear # class
"type": Conv1D(filters=10, kernel_size=2) # cell instance
"type": MyLayer(...) # cell instance
`"kwargs"`: dict
A dictionary of keyword arguments for constructor of the
layer class. Ignored if :attr:`"type"` is a layer instance.
- Arguments named "activation" can be a callable, or a `str` of
the name or module path to the activation function.
- Arguments named "\*_regularizer" and "\*_initializer" can be a
class instance, or a `dict` of hyperparameters of respective
regularizers and initializers. See
- Arguments named "\*_constraint" can be a callable, or a `str`
of the name or full path to the constraint function.
Returns:
A layer instance. If ``hparams["type"]`` is a layer instance, returns it
directly.
Raises:
ValueError: If :attr:`hparams` is `None`.
ValueError: If the resulting layer is not an instance of
:torch_nn:`Module`.
"""
if hparams is None:
raise ValueError("`hparams` must not be `None`.")
layer_type = hparams["type"]
if not is_str(layer_type) and not isinstance(layer_type, type):
layer = layer_type
else:
layer_modules = ["torch.nn", "texar.torch.core", "texar.torch.custom"]
layer_class: Type[nn.Module] = utils.check_or_get_class(
layer_type, layer_modules)
if isinstance(hparams, dict):
if (layer_class.__name__ == "Linear" and
"in_features" not in hparams["kwargs"]):
raise ValueError("\"in_features\" should be specified for "
"\"torch.nn.{}\"".format(layer_class.__name__))
elif (layer_class.__name__ in ["Conv1d", "Conv2d", "Conv3d"] and
"in_channels" not in hparams["kwargs"]):
raise ValueError("\"in_channels\" should be specified for "
"\"torch.nn.{}\"".format(layer_class.__name__))
default_kwargs = _layer_class_to_default_kwargs_map.get(
layer_class, {})
default_hparams = {"type": layer_type, "kwargs": default_kwargs}
hparams = HParams(hparams, default_hparams)
# this case needs to be handled separately because
# :torch_nn:`Sequential`
# does not accept kwargs
if layer_type == "Sequential":
names: List[str] = []
layer = nn.Sequential()
sub_hparams = hparams.kwargs.layers
for hparam in sub_hparams:
sub_layer = get_layer(hparam)
name = utils.uniquify_str(sub_layer._get_name(), names)
names.append(name)
layer.add_module(name=name, module=sub_layer)
else:
layer = utils.get_instance(layer_type, hparams.kwargs.todict(),
layer_modules)
if not isinstance(layer, nn.Module):
raise ValueError("layer must be an instance of `torch.nn.Module`.")
return layer
def _maybe_name_to_id(self, name_or_id):
if is_str(name_or_id):
if name_or_id not in self._name_to_id:
raise ValueError("Unknown data name: {}".format(name_or_id))
return self._name_to_id[name_or_id]
return name_or_id