def get_default_retrieval_decoder(type_context: TypeContext,
rnn_hidden_size: int,
examples: ExamplesStore, replacer: Replacer,
parser: StringParser,
unparser: AstUnparser) -> TreeDecoder:
type_vectorizer = vectorizers.TorchDeepEmbed(type_context.get_type_count(),
rnn_hidden_size)
rnn_cell = TreeRNNCell(rnn_hidden_size, rnn_hidden_size)
latent_store = make_latent_store_from_examples(examples, rnn_hidden_size,
replacer, parser, unparser)
action_selector = RetrievalActionSelector(latent_store, type_context, 0.25)
return TreeRNNDecoder(rnn_cell, action_selector, type_vectorizer,
type_context)
def get_default_nonretrieval_decoder(type_context: TypeContext,
rnn_hidden_size: int) -> TreeDecoder:
object_vectorizer = vectorizers.TorchDeepEmbed(
type_context.get_object_count(), rnn_hidden_size)
ast_embed_size = int(rnn_hidden_size / 2)
type_vectorizer = vectorizers.TorchDeepEmbed(type_context.get_type_count(),
ast_embed_size)
rnn_cell = TreeRNNCellLSTM(ast_embed_size, rnn_hidden_size)
#rnn_cell = TreeCellOnlyAttn(rnn_hidden_size, rnn_hidden_size)
#rnn_cell = TreeRNNCellGRU(rnn_hidden_size, rnn_hidden_size)
action_selector = SimpleActionSelector(
rnn_cell.output_size,
objectselector.get_default_object_selector(type_context,
object_vectorizer),
type_context)
return TreeRNNDecoder(rnn_cell, action_selector, type_vectorizer,
type_context)
def __init__(self, type_context: TypeContext):
self._type_to_impl_tensor = [None] * type_context.get_type_count()
for typ in type_context.get_all_types():
self._type_to_impl_tensor[typ.ind] = \
torch.LongTensor([impl.ind for impl in type_context.get_implementations(typ)])
