def _build_layers(self,
layers: Optional[nn.ModuleList] = None,
layer_hparams: Optional[List[
Union[HParams, Dict[str, Any]]]] = None):
r"""Builds layers.
Either :attr:`layer_hparams` or :attr:`layers` must be
provided. If both are given, :attr:`layers` will be used.
Args:
layers (optional): A list of layer instances supplied as an instance
of :torch_nn:`ModuleList`.
layer_hparams (optional): A list of layer hparams, each to which
is fed to :func:`~texar.torch.core.layers.get_layer` to create
the layer instance.
"""
if layers is not None:
self._layers = layers
else:
if layer_hparams is None:
raise ValueError(
'Either `layer` or `layer_hparams` is required.')
self._layers = nn.ModuleList()
for _, hparams in enumerate(layer_hparams):
self._layers.append(get_layer(hparams=hparams))
for layer in self._layers:
layer_name = uniquify_str(layer.__class__.__name__,
self._layer_names)
self._layer_names.append(layer_name)
self._layers_by_name[layer_name] = layer
def _build_dense_output_layer(cell_output_size: int,
hparams: HParams) -> Optional[nn.Sequential]:
r"""Build the output layers.
Args:
cell_output_size: The output size of the rnn cell.
hparams (dict or HParams): Hyperparameters. Missing hyperparameters
will be set to default values. See
:meth:`default_hparams` for the hyperparameter structure and
default values.
Returns:
A :torch_nn:`Sequential` module containing the output layers.
"""
nlayers = hparams.num_layers
if nlayers <= 0:
return None
layer_size = _to_list(hparams.layer_size, 'output_layer.layer_size',
nlayers)
dropout_layer_ids = _to_list(hparams.dropout_layer_ids)
other_kwargs = hparams.other_dense_kwargs or {}
if isinstance(other_kwargs, HParams):
other_kwargs = other_kwargs.todict()
if not isinstance(other_kwargs, dict):
raise ValueError(
"hparams 'output_layer.other_dense_kwargs' must be a dict.")
output_layers: List[nn.Module] = []
for i in range(nlayers):
if i in dropout_layer_ids:
# TODO: Variational dropout is not implemented.
output_layers.append(nn.Dropout(p=hparams.dropout_rate))
dense_layer = nn.Linear(
in_features=(cell_output_size if i == 0 else layer_size[i - 1]),
out_features=layer_size[i],
**other_kwargs)
output_layers.append(dense_layer)
if i == nlayers - 1:
activation = hparams.final_layer_activation
else:
activation = hparams.activation
if activation is not None:
layer_hparams = {"type": activation, "kwargs": {}}
activation_layer = layers.get_layer(hparams=layer_hparams)
output_layers.append(activation_layer)
if nlayers in dropout_layer_ids:
output_layers.append(nn.Dropout(p=hparams.dropout_rate))
return nn.Sequential(*output_layers)
def test_get_layer(self):
r"""Tests :func:`texar.torch.core.layers.get_layer`.
"""
hparams = {"type": "Conv1d",
"kwargs": {"in_channels": 16,
"out_channels": 32,
"kernel_size": 2}
}
layer = layers.get_layer(hparams)
self.assertIsInstance(layer, nn.Conv1d)
hparams = {
"type": "MergeLayer",
"kwargs": {
"layers": [
{"type": "Conv1d",
"kwargs": {"in_channels": 16,
"out_channels": 32,
"kernel_size": 2}},
{"type": "Conv1d",
"kwargs": {"in_channels": 16,
"out_channels": 32,
"kernel_size": 2}}
]
}
}
layer = layers.get_layer(hparams)
self.assertIsInstance(layer, layers.MergeLayer)
hparams = {"type": "Conv1d",
"kwargs": {"in_channels": 16,
"out_channels": 32,
"kernel_size": 2}
}
layer = layers.get_layer(hparams)
self.assertIsInstance(layer, nn.Conv1d)
hparams = {
"type": nn.Conv1d(in_channels=16, out_channels=32, kernel_size=2)
}
layer = layers.get_layer(hparams)
self.assertIsInstance(layer, nn.Conv1d)
def append_layer(self, layer: Union[nn.Module, HParams, Dict[str, Any]]):
r"""Appends a layer to the end of the network.
Args:
layer: A subclass of :torch_nn:`Module`, or a dict of layer
hyperparameters.
"""
layer_ = layer
if not isinstance(layer_, nn.Module):
layer_ = get_layer(hparams=layer_)
self._layers.append(layer_)
layer_name = uniquify_str(layer_.__class__.__name__, self._layer_names)
self._layer_names.append(layer_name)
self._layers_by_name[layer_name] = layer_
